Rozprawy doktorskie: „PSO (PRATICLE SWARM OPTIMIZATION)”

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SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION". Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.

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COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of civilians and affected millions around the world. Mostly as result, numerous technologies that enable both the rapid and accurate identification of COVID-19 illnesses will provide much assistance to healthcare practitioners. A machine learning- based approach is used for the detection of COVID-19. In general, artificial intelligence (AI) approaches have yielded positive outcomes in healthcare visual processing and analysis. CXR is the digital image processing method that plays a vital role in the analysis of Covid-19 disease. Due to the maximum accessibility of huge scale annotated image databases, excessive success has been done using multiclass support vector machines for image classification. Image classification is the main challenge to detect medical diagnosis. The existing work used CNN with a transfer learning mechanism that can give a solution by transferring information from GENETIC object recognition tasks. The DeTrac method has been used to detect the disease in CXR images. DeTrac method accuracy achieved 93.1~ 97 percent. In this proposed work, the hybridization PSO+MSVM method has worked with irregularities in the CXR images database by studying its group distances using a group or class mechanism. At the initial phase of the process, a median filter is used for the noise reduction from the image. Edge detection is an essential step in the process of COVID-19 detection. The canny edge detector is implemented for the detection of edges in the chest x-ray images. The PCA (Principal Component Analysis) method is implemented for the feature extraction phase. There are multiple features extracted through PCA and the essential features are optimized by an optimization technique known as swarm optimization is used for feature optimization. For the detection of COVID-19 through CXR images, a hybrid multi-class support vector machine technique is implemented. The PSO (particle swarm optimization) technique is used for feature optimization. The comparative analysis of various existing techniques is also depicted in this work. The proposed system has achieved an accuracy of 97.51 percent, SP of 97.49 percent, and 98.0 percent of SN. The proposed system is compared with existing systems and achieved better performance and the compared systems are DeTrac, GoogleNet, and SqueezeNet.

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Urade, Hemlata S., i Rahila Patel. "Performance Evaluation of Dynamic Particle Swarm Optimization". IJCSN, 2012. http://hdl.handle.net/10150/283597.

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Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Unconstrained optimization problems can be formulated as a D-dimensional minimization problem as follows: Min f (x) x=[x1+x2+……..xD] where D is the number of the parameters to be optimized. subjected to: Gi(x) <=0, i=1…q Hj(x) =0, j=q+1,……m Xε [Xmin, Xmax]D, q is the number of inequality constraints and m-q is the number of equality constraints. The particle swarm optimizer (PSO) is a relatively new technique. Particle swarm optimizer (PSO), introduced by Kennedy and Eberhart in 1995, [1] emulates flocking behavior of birds to solve the optimization problems.
In this paper the concept of dynamic particle swarm optimization is introduced. The dynamic PSO is different from the existing PSO’s and some local version of PSO in terms of swarm size and topology. Experiment conducted for benchmark functions of single objective optimization problem, which shows the better performance rather the basic PSO. The paper also contains the comparative analysis for Simple PSO and Dynamic PSO which shows the better result for dynamic PSO rather than simple PSO.

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Cleghorn, Christopher Wesley. "A Generalized theoretical deterministic particle swarm model". Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/33333.

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Particle swarm optimization (PSO) is a well known population-based search algorithm, originally developed by Kennedy and Eberhart in 1995. The PSO has been utilized in a variety of application domains, providing a wealth of empirical evidence for its effectiveness as an optimizer. The PSO itself has undergone many alterations subsequent to its inception, some of which are fundamental to the PSO's core behavior, others have been more application specific. The fundamental alterations to the PSO have to a large extent been a result of theoretical analysis of the PSO's particle's long term trajectory. The most obvious example, is the need for velocity clamping in the original PSO. While there were empirical fndings that suggested that each particle's velocity was increasing at a rapid rate, it was only once a solid theoretical study was performed that the reason for the velocity explosion was understood. There has been a large amount of theoretical research done on the PSO, both for the deterministic model, and more recently for the stochastic model. This thesis presents an extension to the theoretical deterministic PSO model. Under the extended model, conditions for particle convergence to a point are derived. At present all theoretical PSO research is done under the stagnation assumption, in some form or another. The analysis done under the stagnation assumption is one where the personal best and neighborhood best are assumed to be non-changing. While analysis under the stagnation assumption is very informative, it could never provide a complete description of a PSO's behavior. Furthermore, the assumption implicitly removes the notion of a social network structure from the analysis. The model used in this thesis greatly weakens the stagnation assumption, by instead assuming that each particle's personal best and neighborhood best can occupy an arbitrarily large number of unique positions. Empirical results are presented to support the theoretical fndings.
Dissertation (MSc)--University of Pretoria, 2013.
gm2014
Computer Science
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Amiri, Mohammad Reza Shams, i Sarmad Rohani. "Automated Camera Placement using Hybrid Particle Swarm Optimization". Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3326.

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Context. Automatic placement of surveillance cameras' 3D models in an arbitrary floor plan containing obstacles is a challenging task. The problem becomes more complex when different types of region of interest (RoI) and minimum resolution are considered. An automatic camera placement decision support system (ACP-DSS) integrated into a 3D CAD environment could assist the surveillance system designers with the process of finding good camera settings considering multiple constraints. Objectives. In this study we designed and implemented two subsystems: a camera toolset in SketchUp (CTSS) and a decision support system using an enhanced Particle Swarm Optimization (PSO) algorithm (HPSO-DSS). The objective for the proposed algorithm was to have a good computational performance in order to quickly generate a solution for the automatic camera placement (ACP) problem. The new algorithm benefited from different aspects of other heuristics such as hill-climbing and greedy algorithms as well as a number of new enhancements. Methods. Both CTSS and ACP-DSS were designed and constructed using the information technology (IT) research framework. A state-of-the-art evolutionary optimization method, Hybrid PSO (HPSO), implemented to solve the ACP problem, was the core of our decision support system. Results. The CTSS is evaluated by some of its potential users after employing it and later answering a conducted survey. The evaluation of CTSS confirmed an outstanding satisfactory level of the respondents. Various aspects of the HPSO algorithm were compared to two other algorithms (PSO and Genetic Algorithm), all implemented to solve our ACP problem. Conclusions. The HPSO algorithm provided an efficient mechanism to solve the ACP problem in a timely manner. The integration of ACP-DSS into CTSS might aid the surveillance designers to adequately and more easily plan and validate the design of their security systems. The quality of CTSS as well as the solutions offered by ACP-DSS were confirmed by a number of field experts.
Sarmad Rohani: 004670606805 Reza Shams: 0046704030897

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Brits, Riaan. "Niching strategies for particle swarm optimization". Diss., Pretoria : [s.n.], 2002. http://upetd.up.ac.za/thesis/available/etd-02192004-143003.

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Cleghorn, Christopher Wesley. "Particle swarm optimization : empirical and theoretical stability analysis". Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/61265.

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Particle swarm optimization (PSO) is a well-known stochastic population-based search algorithm, originally developed by Kennedy and Eberhart in 1995. Given PSO's success at solving numerous real world problems, a large number of PSO variants have been proposed. However, unlike the original PSO, most variants currently have little to no existing theoretical results. This lack of a theoretical underpinning makes it difficult, if not impossible, for practitioners to make informed decisions about the algorithmic setup. This thesis focuses on the criteria needed for particle stability, or as it is often refereed to as, particle convergence. While new PSO variants are proposed at a rapid rate, the theoretical analysis often takes substantially longer to emerge, if at all. In some situation the theoretical analysis is not performed as the mathematical models needed to actually represent the PSO variants become too complex or contain intractable subproblems. It is for this reason that a rapid means of determining approximate stability criteria that does not require complex mathematical modeling is needed. This thesis presents an empirical approach for determining the stability criteria for PSO variants. This approach is designed to provide a real world depiction of particle stability by imposing absolutely no simplifying assumption on the underlying PSO variant being investigated. This approach is utilized to identify a number of previously unknown stability criteria. This thesis also contains novel theoretical derivations of the stability criteria for both the fully informed PSO and the unified PSO. The theoretical models are then empirically validated utilizing the aforementioned empirical approach in an assumption free context. The thesis closes with a substantial theoretical extension of current PSO stability research. It is common practice within the existing theoretical PSO research to assume that, in the simplest case, the personal and neighborhood best positions are stagnant. However, in this thesis, stability criteria are derived under a mathematical model where by the personal best and neighborhood best positions are treated as convergent sequences of random variables. It is also proved that, in order to derive stability criteria, no weaker assumption on the behavior of the personal and neighborhood best positions can be made. The theoretical extension presented caters for a large range of PSO variants.
Thesis (PhD)--University of Pretoria, 2017.
Computer Science
PhD
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Veselý, Filip. "Aplikace optimalizační metody PSO v podnikatelství". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2010. http://www.nusl.cz/ntk/nusl-222445.

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This work deals with two optimization problems, traveling salesman problem and cluster analysis. Solution of these optimization problems are applied on INVEA-TECH company needs. It shortly describes questions of optimization and some optimization techniques. Closely deals with swarm intelligence, strictly speaking particle swarm intelligence. Part of this work is recherché of variants of particle swarm optimization algorithm. The second part describes PSO algorithms solving clustering problem and traveling salesman problem and their implementation in Matlab language.

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Franz, Wayne. "Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition". Springer, 2013. http://hdl.handle.net/1993/23842.

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Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach executes PSO and GAs simultaneously in different swarms, and shows advantages when arranged in a star topology, particularly with a central GA. A static scheme executes in series, with a GA performing the exploration followed by MPSO for exploitation. Finally, the last approach dynamically alternates between algorithms. Hybrid algorithms are well-suited for parallelization, but exhibit tradeoffs between performance and solution quality.

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Lai, Chun-Hau. "Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO". Tesis, Universidad de Chile, 2012. http://www.repositorio.uchile.cl/handle/2250/111954.

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Magíster en Ciencias, Mención Computación
Este trabajo de tesis está dedicado al diseño e implementación de algoritmos aproximados que permiten explorar las mejores soluciones para el problema de Clustering Balanceado, el cual consiste en dividir un conjunto de n puntos en k clusters tal que cada cluster tenga como m ́ınimo ⌊ n ⌋ puntos, k y éstos deben estar lo más cercano posible al centroide de cada cluster. Estudiamos los algoritmos existentes para este problema y nuestro análisis muestra que éstos podrían fallar en entregar un resultado óptimo por la ausencia de la evaluación de los resultados en cada iteración del algoritmo. Entonces, recurrimos al concepto de Particles Swarms, que fue introducido inicialmente para simular el comportamiento social humano y que permite explorar todas las posibles soluciones de manera que se aproximen a la óptima rápidamente. Proponemos cuatro algoritmos basado en Particle Swarm Optimization (PSO): PSO-Hu ́ngaro, PSO-Gale-Shapley, PSO-Aborci ́on-Punto-Cercano y PSO-Convex-Hull, que aprovechan la característica de la generación aleatoria de los centroides por el algoritmo PSO, para asignar los puntos a estos centroides, logrando una solución más aproximada a la óptima. Evaluamos estos cuatro algoritmos con conjuntos de datos distribuidos en forma uniforme y no uniforme. Se encontró que para los conjuntos de datos distribuidos no uniformemente, es impredecible determinar cuál de los cuatro algoritmos propuestos llegaría a tener un mejor resultado de acuerdo al conjunto de métricas (intra-cluster-distancia, índice Davies-Doublin e índice Dunn). Por eso, nos concentramos con profundidad en el comportamiento de ellos para los conjuntos de datos distribuidos en forma uniforme. Durante el proceso de evaluación se descubrió que la formación de los clusters balanceados de los algoritmos PSO-Absorcion-Puntos-Importantes y PSO-Convex-Hull depende fuertemente del orden con que los centroides comienzan a absorber los puntos más cercanos. En cambio, los algoritmos PSO-Hungaro y PSO-Gale-Shapley solamente dependen de los centroides generados y no del orden de los clusters a crear. Se pudo concluir que el algoritmo PSO-Gale-Shapley presenta el rendimiento menos bueno para la creación de clusters balanceados, mientras que el algoritmo PSO-Hungaro presenta el rendimiento más eficiente para lograr el resultado esperado. Éste último está limitado al tamaño de los datos y la forma de distribución. Se descubrió finalmente que, para los conjuntos de datos de tamaños grandes, independiente de la forma de distribución, el algoritmo PSO-Convex-Hull supera a los demás, entregando mejor resultado según las métricas usadas.

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Oldewage, Elre Talea. "The perils of particle swarm optimization in high dimensional problem spaces". Diss., University of Pretoria, 2005. http://hdl.handle.net/2263/66233.

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Particle swarm optimisation (PSO) is a stochastic, population-based optimisation algorithm. PSO has been applied successfully to a variety of domains. This thesis examines the behaviour of PSO when applied to high dimensional optimisation problems. Empirical experiments are used to illustrate the problems exhibited by the swarm, namely that the particles are prone to leaving the search space and never returning. This thesis does not intend to develop a new version of PSO speci cally for high dimensional problems. Instead, the thesis investigates why PSO fails in high dimensional search spaces. Four di erent types of approaches are examined. The rst is the application of velocity clamping to prevent the initial velocity explosion and to keep particles inside the search space. The second approach selects values for the acceleration coe cients and inertia weights so that particle movement is restrained or so that the swarm follows particular patterns of movement. The third introduces coupling between problem variables, thereby reducing the swarm's movement freedom and forcing the swarm to focus more on certain subspaces within the search space. The nal approach examines the importance of initialisation strategies in controlling the swarm's exploration to exploitation ratio. The thesis shows that the problems exhibited by PSO in high dimensions, particularly unwanted particle roaming, can not be fully mitigated by any of the techniques examined. The thesis provides deeper insight into the reasons for PSO's poor performance by means of extensive empirical tests and theoretical reasoning.
Dissertation (MSc)--University of Pretoria, 2017.
Computer Science
MSc
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Leonard, Barend Jacobus. "Critical analysis of angle modulated particle swarm optimisers". Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/61548.

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This dissertation presents an analysis of the angle modulated particle swarm optimisation (AMPSO) algorithm. AMPSO is a technique that enables one to solve binary optimisation problems with particle swarm optimisation (PSO), without any modifications to the PSO algorithm. While AMPSO has been successfully applied to a range of optimisation problems, there is little to no understanding of how and why the algorithm might fail. The work presented here includes in-depth theoretical and emprical analyses of the AMPSO algorithm in an attempt to understand it better. Where problems are identified, they are supported by theoretical and/or empirical evidence. Furthermore, suggestions are made as to how the identified issues could be overcome. In particular, the generating function is identified as the main cause for concern. The generating function in AMPSO is responsible for generating binary solutions. However, it is shown that the increasing frequency of the generating function hinders the algorithm’s ability to effectively exploit the search space. The problem is addressed by introducing methods to construct different generating functions, and to quantify the quality of arbitrary generating functions. In addition to this, a number of other problems are identified and addressed in various ways. The work concludes with an empirical analysis that aims to identify which of the various suggestions made throughout this dissertatioin hold substantial promise for further research.
Dissertation (MSc)--University of Pretoria, 2017.
Computer Science
MSc
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Scheepers, Christiaan. "Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer". Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/64041.

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An exploratory analysis in low-dimensional objective space of the vector evaluated particle swarm optimization (VEPSO) algorithm is presented. A novel visualization technique is presented and applied to perform the exploratory analysis. The exploratory analysis together with a quantitative analysis revealed that the VEPSO algorithm continues to explore without exploiting the well-performing areas of the search space. A detailed investigation into the influence that the choice of archive implementation has on the performance of the VEPSO algorithm is presented. Both the Pareto-optimal front (POF) solution diversity and convergence towards the true POF is considered during the investigation. Attainment surfaces are investigated for their suitability in efficiently comparing two multi-objective optimization (MOO) algorithms. A new measure to objectively compare algorithms in multi-dimensional objective space, based on attainment surfaces, is presented. This measure, referred to as the porcupine measure, adapts the attainment surface measure by using a statistical test along with weighted intersection lines. Loosely based on the VEPSO algorithm, the multi-guided particle swarm optimization (MGPSO) algorithm is presented and evaluated. The results indicate that the MGPSO algorithm overcomes the weaknesses of the VEPSO algorithm and also outperforms a number of state of the art MOO algorithms on at least two benchmark test sets.
Thesis (PhD)--University of Pretoria, 2017.
Computer Science
PhD
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Mazin, Asim Mohamed. "REDUCING THE PEAK TO AVERAGE POWER RATIO OF MIMO-OFDM USING Particle SWARM OPTIMIZATION BASED PTS". OpenSIUC, 2013. https://opensiuc.lib.siu.edu/theses/1130.

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Asim M. Mazin, for the Master of Science degree in Electrical and Computer Engineering, presented on Mar 27, 2013, at Southern Illinois University Carbondale. TITLE: REDUCING THE PEAK TO AVERAGE POWER RATIO OF MIMO-OFDM USING PSO BASED PTS. MAJOR PROFESSOR: Dr. Garth V. Crosby, In this thesis we proposed PSO based PTS to accomplish the lowest Peak-to-Average Power Ratio of MIMO-OFDM system. We applied the PSO based PTS on each antenna of the system in order to find the optimal phase factors which is a straightforward method to get the minimum PAPR in such a system. The performance of PSO based PTS algorithm in MIMO-OFDM with a wide range of phase factor tends to give a high performance according to the simulation results. In addition, there is no need to increase the number of particles of the PSO algorithm to enhance the performance of the system, which keeps the complexity of finding the minimum PAPR reasonable.

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LIMA, Natália Flora De. "Frankenstein PSO na definição das arquiteturas e ajustes dos pesos e uso de PSO heterogêneo no treinamento de redes neurais feed-forward". Universidade Federal de Pernambuco, 2011. https://repositorio.ufpe.br/handle/123456789/17738.

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Este trabalho apresenta dois novos algoritmos, PSO-FPSO e FPSO-FPSO, para a otimização global de redes neurais MLP (do inglês Multi Layer Perceptron) do tipo feed-forward. O propósito destes algoritmos é otimizar de forma simultânea as arquiteturas e pesos sinápticos, objetivando melhorar a capacidade de generalização da rede neural artificial (RNA). O processo de otimização automática das arquiteturas e pesos de uma rede neural vem recebendo grande atenção na área de aprendizado supervisionado, principalmente em problemas de classificação de padrões. Além dos Algoritmos Genéticos, Busca Tabu, Evolução Diferencial, Recozimento simulado que comumente são empregados no treinamento de redes neurais podemos citar abordagens populacionais como a otimização por colônia de formigas, otimização por colônia de abelhas e otimização por enxame de partículas que vêm sendo largamente utilizadas nesta tarefa. A metodologia utilizada neste trabalho trata da aplicação de dois algoritmos do tipo PSO, sendo empregados na otimização das arquiteturas e na calibração dos pesos das conexões. Nesta abordagem os algoritmos são executados de forma alternada e por um número definido de vezes. Ainda no processo de ajuste dos pesos de uma rede neural MLP foram realizados experimentos com enxame de partículas heterogêneos, que nada mais é que a junção de dois ou mais PSOs de tipos diferentes. Para validar os experimentos com os enxames homogêneos foram utilizadas sete bases de dados para problemas de classificação de padrões, são elas: câncer, diabetes, coração, vidros, cavalos, soja e tireóide. Para os experimentos com enxames heterogêneos foram utilizadas três bases, a saber: câncer, diabetes e coração. O desempenho dos algoritmos foi medido pela média do erro percentual de classificação. Algoritmos da literatura são também considerados. Os resultados mostraram que os algoritmos investigados neste trabalho obtiveram melhor acurácia de classificação quando comparados com os algoritmos da literatura mencionados neste trabalho.
This research presents two new algorithms, PSO-FPSO e FPSO-FPSO, that can be used in feed-forward MLP (Multi Layer Perceptron) neural networks for global optimization. The purpose of these algorithms is to optimize architectures and synaptic weight, at same time, to improve the capacity of generalization from Artificial Neural Network (ANN). The automatic optimization process of neural network’s architectures and weights has received much attention in supervised learning, mainly in pattern classification problems. Besides the Genetic Algorithms, Tabu Search, Differential Evolution, Simulated Annealing that are commonly used in the training of neural networks we can mentioned population approaches such Ant Colony Optimization, Bee Colony Optimization and Particle Swarm Optimization that have been widely used this task. The methodology applied in this research reports the use of two PSO algorithms, used in architecture optimization and connection weight adjust. In this approach the algorithms are performed alternately and by predefined number of times. Still in the process of adjusting the weights of a MLP neural network experiments were performed with swarm of heterogeneous particles, which is nothing more than the joining of two or more different PSOs. To validate the experiments with homogeneous clusters were used seven databases for pattern classification problems, they are: cancer, diabetes, heart, glasses, horses, soy and thyroid. For the experiments with heterogeneous clusters were used three bases, namely cancer, diabetes and heart. The performance of the algorithms was measured by the average percentage of misclassification, literature algorithms are also considered. The results showed that the algorithms investigated in this research had better accuracy rating compared with some published algorithms.

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Barla-Szabo, Daniel. "A study of gradient based particle swarm optimisers". Diss., University of Pretoria, 2010. http://hdl.handle.net/2263/29927.

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Gradient-based optimisers are a natural way to solve optimisation problems, and have long been used for their efficacy in exploiting the search space. Particle swarm optimisers (PSOs), when using reasonable algorithm parameters, are considered to have good exploration characteristics. This thesis proposes a specific way of constructing hybrid gradient PSOs. Heterogeneous, hybrid gradient PSOs are constructed by allowing the gradient algorithm to optimise local best particles, while the PSO algorithm governs the behaviour of the rest of the swarm. This approach allows the distinct algorithms to concentrate on performing the separate tasks of exploration and exploitation. Two new PSOs, the Gradient Descent PSO, which combines the Gradient Descent and PSO algorithms, and the LeapFrog PSO, which combines the LeapFrog and PSO algorithms, are introduced. The GDPSO represents arguably the simplest hybrid gradient PSO possible, while the LeapFrog PSO incorporates the more sophisticated LFOP1(b) algorithm, exhibiting a heuristic algorithm design and dynamic time step adjustment mechanism. The strong tendency of these hybrids to prematurely converge is examined, and it is shown that by modifying algorithm parameters and delaying the introduction of gradient information, it is possible to retain strong exploration capabilities of the original PSO algorithm while also benefiting from the exploitation of the gradient algorithms.
Dissertation (MSc)--University of Pretoria, 2010.
Computer Science
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Maripi, Jagadish Kumar. "AN EFFECTIVE PARALLEL PARTICLE SWARM OPTIMIZATION ALGORITHM AND ITS PERFORMANCE EVALUATION". OpenSIUC, 2010. https://opensiuc.lib.siu.edu/theses/275.

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Population-based global optimization algorithms including Particle Swarm Optimization (PSO) have become popular for solving multi-optima problems much more efficiently than the traditional mathematical techniques. In this research, we present and evaluate a new parallel PSO algorithm that provides a significant performance improvement as compared to the serial PSO algorithm. Instead of merely assigning parts of the task of serial version to several processors, the new algorithm places multiple swarms on the available nodes in which operate independently, while collaborating on the same task. With the reduction of the communication bottleneck as well the ability to manipulate the individual swarms independently, the proposed approach outperforms the original PSO algorithm and still maintains the simplicity and ease of implementation.

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Palangpour, Parviz Michael. "FFGA implementation of PSO algorithm and neural networks". Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Palangpour_09007dcc8078a58e.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2010.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 8, 2010) Includes bibliographical references (p. 76-78).

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Endo, Makoto. "Wind Turbine Airfoil Optimization by Particle Swarm Method". Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1285774101.

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Ullmann, Matheus Rudolfo Diedrich. "Formação de grupos em MOOCs utilizando Particle Swarm Optimization". Universidade Federal de Goiás, 2016. http://repositorio.bc.ufg.br/tede/handle/tede/5609.

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Made available in DSpace on 2016-06-01T11:00:53Z (GMT). No. of bitstreams: 2 Dissertação - Matheus Rudolfo Diedrich Ullmann - 2016.pdf: 1264745 bytes, checksum: 65f8378224bd7fd700216a920f2da7a0 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2016-02-26
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
The MassiveOpenOnlineCourses(MOOCs)areonlinecourseswithopenenrollment that involvingahugeamountofstudentsfromdifferentlocations,withdifferentback- grounds andinterests.Thelargenumberofstudentsimpliesahugeandunmanageable number ofinteractions.Thisfact,alongwiththedifferentinterestsofstudents,resulting in low-qualityinteractions.Duetothelargenumberofstudents,alsobecomesunviable composition manuallylearninggroups.DuetothesecharacteristicspresentinMOOCs, a methodforforminggroupswasdevelopedinthiswork,asanattempttoattendthedi- chotomy existsbetweenthecollective,whichinvolvestheformationofanonlinelearning community onamassivescale,andindividual,withdifferentinterests,priorknowledge and expectationsanddifferentleadershipprofiles.Fortheformationofgroups,anadapta- tion ofParticleSwarmOptimizationalgorithmwasproposedbasedonthreecriteria,kno- wledge level,interestsandleadershipprofiles,formingthengroupswithdifferentlevels of knowledge,similarinterestsanddistributedleadership,providingbetterinteractionand knowledgeconstruction.Werecreatedtwovariationsoftheproblem,withfivestudents and theothersix.Basedoncomputationaltests,thealgorithmdemonstratedthatableto attend thegroupingcriteriainasatisfactorycomputingtimeandismoreefficientthanthe model randomgroupsformation.Thetestsalsodemonstratedthatthealgorithmisrobust taking intoaccountthevariousdatasetsanditerationsvariations.Toevaluatethequality of interactionsandknowledgebuildingingroupsformedbythemethod,Acasestudy wasconducted;andfortheanalysisofthecollecteddiscourses,itwastakenasthebasis twomodelsofdiscourseanalysisfoundintheliterature.Theresultsofthecasestudy demonstrated thatthegroupsformedbytheproposedmethodachievedthebestresultsin the interactionsandknowledgeconstruction,whencomparedwithgroupsthatdonotuse it.
Os Massive OpenOnlineCourses (MOOCs) sãocursos online com inscriçõesabertas que envolvemumaenormequantidadedeestudantesdediferenteslocalidades,comdife- rentes backgrounds e interesses.Ograndenúmerodealunosimplicaemumaenormee não gerenciávelquantidadedeinterações.Estefato,juntamentecomosinteressesdife- rentes dosalunos,resultaeminteraçõesdebaixaqualidade.Devidoàgrandequantidade de alunos,tambémtorna-seinviávelacomposiçãodegruposdeaprendizagemdeforma manual. DevidoàessascaracterísticaspresentesnosMOOCs,ummétodoparaformação de gruposfoidesenvolvidonestetrabalho,comoumatentativaparaatenderadicoto- mia queexisteentreocoletivo,queenvolveaformaçãodeumacomunidade online de aprendizagem emumaescalamaciça,eoindividual,comdiferentesinteresses,conhe- cimentos prévioseexpectativasecomdiferentesperfisdeliderança.Paraaformação dos grupos,umaadaptaçãodoalgoritmo ParticleSwarmOptimization foi propostacom base emtrêscritérios,níveldeconhecimento,interesseseperfisdeliderança,formando então gruposcomníveisdeconhecimentodiferentes,interessessemelhanteseliderança distribuída,proporcionandoumamelhorinteraçãoeconstruçãodeconhecimento.Foram criadas duasvariaçõesdoproblema,umacomcincoalunoseoutracomseis.Combase em testescomputacionais,oalgoritmodemonstrouqueconsegueatenderoscritériosde agrupamento emumtempodecomputaçãosatisfatórioeémaiseficientequeomodelode formação degruposaleatório.Ostestesdemonstraramtambémqueoalgoritmoérobusto levandoemcontaosvariadosconjuntosdedadosevariaçõesdeiterações.Paraavaliara qualidade dasinteraçõeseaconstruçãodeconhecimentonosgruposformadospelomé- todo, umestudodecasofoirealizado;eparaaanálisedosdiscursoscoletados,tomou-se como basedoismodelosdeanálisedediscursopresentesnaliteratura.Oresultadodo estudo decasodemonstrouqueosgruposformadospelométodopropostoobtiveramos melhores resultadosnasinteraçõeseconstruçãodoconhecimento,quandocomparados com osgruposquenãooutilizaram.

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Melo, Leonardo Alves Moreira de. "Comparação de algoritmos de enxame de partículas para otimização de problemas em larga escala". Universidade Federal de Goiás, 2018. http://repositorio.bc.ufg.br/tede/handle/tede/9108.

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Made available in DSpace on 2018-11-29T11:09:58Z (GMT). No. of bitstreams: 2 Dissertação - Leonardo Alves Moreira de Melo - 2018.pdf: 2693689 bytes, checksum: 850fbad5a82099825d2478ba3415dcac (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-10-26
Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG
In order to address an issue concerning the increasing number of algorithms based on particle swarm optimization (PSO) applied to solve large-scale optimization problems (up to 2000 variables), this article presents analysis and comparisons among five state- of-the-art PSO algorithms (CCPSO2, LSS- PSO, OBL-PSO, SPSO and VCPSO). Tests were performed to illustrate the e ciency and feasibility of using the algorithms for this type of problem. Six benchmark functions most commonly used in the literature (Ackley 1, Griewank, Rastrigin, Rosenbrock, Schwefel 1.2 and Sphere) were tested. The experiments were performed using a high-dimensional problem (500 variables), varying the number of particles (50, 100 and 200 particles) in each algorithm, thus increasing the computational complexity. The analysis showed that the CCPSO2 and OBL-PSO algorithms found significantly better solutions than the other algorithms for more complex multimodal problems (which most resemble realworld problems). However, considering unimodal functions, the CCPSO2 algorithm stood out before the others. Our results and experimental analysis suggest that CCPSO2 and OBL- PSO seem to be highly competitive optimization algorithms to solve complex and multimodal optimization problems.
O número de algoritmos baseados na otimização por enxame de partículas (PSO) aplicados para resolver problemas de otimização em grande escala (até 2.000 variáveis) aumentou significativamente. Este trabalho apresenta análises e comparações entre cinco algoritmos (CCPSO2, LSSPSO, OBL-CPSO, SPSO e VCPSO). Testes foram realizados para ilustrar a eficiência e viabilidade de usar os algoritmos para resolver problemas em larga escala. Seis funções de referência que são comumente utilizadas na literatura (Ackley 1, Griewank, Rastrigin, Rosenbrock, Schwefel 1.2 e Sphere) foram utilizadas para testar a performancedesses algoritmos. Os experimentos foram realizados utilizando um problema de alta dimensionalidade (500 variáveis), variando o número de partículas (50, 100 e 200 partículas) em cada algoritmo, aumentando assim a complexidade computacional. A análise mostrou que os algoritmos CCPSO2 e OBL-CPSO mostraram-se significativamente melhores que os outros algoritmos para problemas multimodais mais complexos (que mais se assemelham a problemas reais). No entanto, considerando as funções unimodais, o algoritmo CCPSO2 destacou-se perante os demais. Nossos resultados e análises experimentais sugerem que o CCPSO2 e o OBL-CPSO são algoritmos de otimização altamente competitivos para resolver problemas de otimização complexos e multimodais em larga escala.

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Silva, Neto Otilio Paulo da. "Detecção automática de massas em imagens mamográficas usando particle swarm optimization (PSO) e índice de diversidade funcional". Universidade Federal do Maranhão, 2016. http://tedebc.ufma.br:8080/jspui/handle/tede/298.

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Made available in DSpace on 2016-08-17T14:52:40Z (GMT). No. of bitstreams: 1 Dissertacao-OtilioPauloSilva.pdf: 2236988 bytes, checksum: e67439b623fd83b01f7bcce0020365fb (MD5) Previous issue date: 2016-03-04
Breast cancer is now set on the world stage as the most common among women and the second biggest killer. It is known that diagnosed early, the chance of cure is quite significant, on the other hand, almost late discovery leads to death. Mammography is the most common test that allows early detection of cancer, this procedure can show injury in the early stages also contribute to the discovery and diagnosis of breast lesions. Systems computer aided, have been shown to be very important tools in aid to specialists in diagnosing injuries. This paper proposes a computational methodology to assist in the discovery of mass in dense and nondense breasts. This paper proposes a computational methodology to assist in the discovery of mass in dense and non-dense breasts. Divided into 6 stages, this methodology begins with the acquisition of the acquired breast image Digital Database for Screening Mammography (DDSM). Then the second phase is done preprocessing to eliminate and enhance the image structures. In the third phase is executed targeting with the Particle Swarm Optimization (PSO) to find regions of interest (ROIs) candidates for mass. The fourth stage is reduction of false positives, which is divided into two parts, reduction by distance and clustering graph, both with the aim of removing unwanted ROIs. In the fifth stage are extracted texture features using the functional diversity indicia (FD). Finally, in the sixth phase, the classifier uses support vector machine (SVM) to validate the proposed methodology. The best values found for non-dense breasts, resulted in sensitivity of 96.13%, specificity of 91.17%, accuracy of 93.52%, the taxe of false positives per image 0.64 and acurva free-response receiver operating characteristic (FROC) with 0.98. The best finds for dense breasts hurt with the sensitivity of 97.52%, specificity of 92.28%, accuracy of 94.82% a false positive rate of 0.38 per image and FROC curve 0.99. The best finds with all the dense and non dense breasts Showed 95.36% sensitivity, 89.00% specificity, 92.00% accuracy, 0.75 the rate of false positives per image and 0, 98 FROC curve.
O câncer de mama hoje é configurado no senário mundial como o mais comum entre as mulheres e o segundo que mais mata. Sabe-se que diagnosticado precocemente, a chance de cura é bem significativa, por outro lado, a descoberta tardia praticamente leva a morte. A mamografia é o exame mais comum que permite a descoberta precoce do câncer, esse procedimento consegue mostrar lesões nas fases iniciais, além de contribuir para a descoberta e o diagnóstico de lesões na mama. Sistemas auxiliados por computador, têm-se mostrado ferramentas importantíssimas, no auxilio a especialistas em diagnosticar lesões. Este trabalho propõe uma metodologia computacional para auxiliar na descoberta de massas em mamas densas e não densas. Dividida em 6 fases, esta metodologia se inicia com a aquisição da imagem da mama adquirida da Digital Database for Screening Mammography (DDSM). Em seguida, na segunda fase é feito o pré-processamento para eliminar e realçar as estruturas da imagem. Na terceira fase executa-se a segmentação com o Particle Swarm Optimization (PSO) para encontrar as regiões de interesse (ROIs) candidatas a massa. A quarta fase é a redução de falsos positivos, que se subdivide em duas partes, sendo a redução pela distância e o graph clustering, ambos com o objetivo de remover ROIs indesejadas. Na quinta fase são extraídas as características de textura utilizando os índices de diversidade funcional (FD). Por fim, na sexta fase, utiliza-se o classificador máquina de vetores de suporte (SVM) para validar a metodologia proposta. Os melhores valores achados para as mamas não densas, resultaram na sensibilidade de 96,13%, especificidade de 91,17%, acurácia de 93,52%, a taxe de falsos positivos por imagem de 0,64 e a acurva Free-response Receiver Operating Characteristic (FROC) com 0,98. Os melhores achados para as mamas densas firam com a sensibilidade de 97,52%, especificidade de 92,28%, acurácia de 94,82%, uma taxa de falsos positivos por imagem de 0,38 e a curva FROC de 0,99. Os melhores achados com todas as mamas densas e não densas, apresentaram 95,36% de sensibilidade, 89,00% de especificidade, 92,00% de acurácia, 0,75 a taxa de falsos positivos por imagem e 0,98 a curva FROC.

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van, Wyk Andrich Benjamin. "An Analysis of Overfitting in Particle Swarm Optimised Neural Networks". Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/46273.

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The phenomenon of overfitting, where a feed-forward neural network (FFNN) over trains on training data at the cost of generalisation accuracy is known to be speci c to the training algorithm used. This study investigates over tting within the context of particle swarm optimised (PSO) FFNNs. Two of the most widely used PSO algorithms are compared in terms of FFNN accuracy and a description of the over tting behaviour is established. Each of the PSO components are in turn investigated to determine their e ect on FFNN over tting. A study of the maximum velocity (Vmax) parameter is performed and it is found that smaller Vmax values are optimal for FFNN training. The analysis is extended to the inertia and acceleration coe cient parameters, where it is shown that speci c interactions among the parameters have a dominant e ect on the resultant FFNN accuracy and may be used to reduce over tting. Further, the signi cant e ect of the swarm size on network accuracy is also shown, with a critical range being identi ed for the swarm size for e ective training. The study is concluded with an investigation into the e ect of the di erent activation functions. Given strong empirical evidence, an hypothesis is made that stating the gradient of the activation function signi cantly a ects the convergence of the PSO. Lastly, the PSO is shown to be a very effective algorithm for the training of self-adaptive FFNNs, capable of learning from unscaled data.
Dissertation (MSc)--University of Pretoria, 2014.
tm2015
Computer Science
MSc
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Zuniga, Virgilio. "Bio-inspired optimization algorithms for smart antennas". Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5766.

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This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best set of phases between antenna elements to obtain a desired antenna pattern. This pattern must meet several restraints, for example: Maximizing the power of the main lobe at a desired direction while keeping nulls towards interferers. A series of experiments show that the PSO achieves better and more consistent radiation patterns than the GA in terms of the total area of the antenna pattern. A second set of experiments use the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization algorithms to find the array weights that configure a rectangular array. The results suggest an advantage in performance by reducing the number of iterations taken by the PSO, thus lowering the computational cost. During the development of this thesis, it was found that the initial states and particular parameters of the optimization algorithms affected their overall outcome. The third part of this work deals with the meta-optimization of these parameters to achieve the best results independently from particular initial parameters. Four algorithms were studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal Sampling and Pattern Search performed better to set the initial parameters and obtain the best performance of the bio-inspired methods studied.

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Rakitianskaia, A. S. (Anastassia Sergeevna). "Using particle swarm optimisation to train feedforward neural networks in dynamic environments". Diss., University of Pretoria, 2011. http://hdl.handle.net/2263/28618.

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The feedforward neural network (NN) is a mathematical model capable of representing any non-linear relationship between input and output data. It has been succesfully applied to a wide variety of classification and function approximation problems. Various neural network training algorithms were developed, including the particle swarm optimiser (PSO), which was shown to outperform the standard back propagation training algorithm on a selection of problems. However, it was usually assumed that the environment in which a NN operates is static. Such an assumption is often not valid for real life problems, and the training algorithms have to be adapted accordingly. Various dynamic versions of the PSO have already been developed. This work investigates the applicability of dynamic PSO algorithms to NN training in dynamic environments, and compares the performance of dynamic PSO algorithms to the performance of back propagation. Three popular dynamic PSO variants are considered. The extent of adaptive properties of back propagation and dynamic PSO under different kinds of dynamic environments is determined. Dynamic PSO is shown to be a viable alternative to back propagation, especially under the environments exhibiting infrequent gradual changes. Copyright 2011, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. Please cite as follows: Rakitianskaia, A 2011, Using particle swarm optimisation to train feedforward neural networks in dynamic environments, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-02132012-233212 / > C12/4/406/gm
Dissertation (MSc)--University of Pretoria, 2011.
Computer Science
Unrestricted

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Esseghir, Mohamed Amir. "Metaheuristics for the feature selection problem : adaptive, memetic and swarm approaches". Thesis, Artois, 2011. http://www.theses.fr/2011ARTO0206/document.

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Afin d’améliorer la qualité de prédiction des techniques de classification automatique et de fouilles de données, plusieurs modèles ont été proposés dans la littérature en vue d’extraire des connaissances à partir des données. Toutefois, avec l’expansion des systèmes d’information et des technologies associées, ces techniques d’apprentissage s’avèrent de moins en moins adaptées aux nouvelles tailles et dimensions des données. On s’intéresse dans cette étude aux problèmes de grande dimensionnalité et à l’amélioration du processus d’apprentissage des méthodes de classification à travers les techniques de filtrage et de sélection d’attributs. Le problème « d’identification d’attributs pertinents » (Feature Selection Problem), tel qu’il est défini dans la littérature, relève d’une nature combinatoire. Dans le cadre de cette thèse, on s’est intéressé au développement de nouvelles techniques d’optimisation approchées et spécifiques au problème traité ainsi qu’à l’amélioration d’algorithmes existants. La conception, l’implémentation et l’étude empirique ont montré l’efficacité et la pertinence des métaheuristiques proposées
Although the expansion of storage technologies, networking systems, and information system methodologies, the capabilities of conventional data processing techniques remain limited. The need to knowledge extraction, compact representation and data analysis are highly motivated by data expansion. Nevertheless, learning from data might be a complex task, particularly when it includes noisy, redundant and information-less attributes. Feature Selection (FS) tries to select the most relevant attributes from raw data, and hence guides the construction of final classification models or decision support systems. Selected features should be representative of the underlying data and provide effective usefulness to the targeted learning paradigm (i.e. classification). In this thesis, we investigate different optimization paradigms as well as its adaptation to the requirements of the feature selection challenges, namely the problem combinatorial nature. Both theoritical and empirical aspects were studied, and confirm the effectiveness of the adopted methodology as well as the proposed metaheuristic based approaches

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Němeček, Patrik. "Optimalizační úlohy na bázi částicových hejn (PSO)". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236036.

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This work deals with particle swarm optimization. The theoretic part briefly describes the problem of optimization. The considerable part focuses on the overall description of particle swarm optimization (PSO). The principle, behavior, parameters, structure and modifications of PSO are described. The next part of the work is a recherché of variants of PSO, including hybridizations of PSO. In practical part the dynamic problems are analyzed and new designed algorithm for dynamic problems AHPSO is described (what it is based on, what was inspired, what elements are used and why). Algorithm is executed on the set of tasks (Moving peaks benchmark) and compared with the best publicly available variants of algorithm PSO on dynamic problems so far.

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Grobler, Jacomine. "Particle swarm optimization and differential evolution for multi-objective multiple machine scheduling". Diss., Pretoria : [s.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-05062009-164124/.

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Gasperazzo, Stéfano Terci. "Um algoritmo PSO híbrido para planejamento de caminhos em navegação de veículos utilizando A*". reponame:Repositório Institucional da UFES, 2014. http://repositorio.ufes.br/handle/10/1466.

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Submitted by Maykon Nascimento (maykon.albani@hotmail.com) on 2015-08-03T18:48:30Z No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Um algoritmo PSO híbrido para planejamento de caminhos em navegação de veículos utilizando A.pdf: 2604695 bytes, checksum: ed8f69e49eaefe272bccd6025290c381 (MD5)
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Made available in DSpace on 2015-08-13T21:44:43Z (GMT). No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Um algoritmo PSO híbrido para planejamento de caminhos em navegação de veículos utilizando A.pdf: 2604695 bytes, checksum: ed8f69e49eaefe272bccd6025290c381 (MD5) Previous issue date: 2015
Utilizar robôs autônomos capazes de planejar o seu caminho é um desafio que atrai vários pesquisadores na área de navegação de robôs. Neste contexto, este trabalho tem como objetivo implementar um algoritmo PSO híbrido para o planejamento de caminhos em ambientes estáticos para veículos holonômicos e não holonômicos. O algoritmo proposto possui duas fases: a primeira utiliza o algoritmo A* para encontrar uma trajetória inicial viável que o algoritmo PSO otimiza na segunda fase. Por fim, uma fase de pós planejamento pode ser aplicada no caminho a fim de adaptá-lo às restrições cinemáticas do veículo não holonômico. O modelo Ackerman foi considerado para os experimentos. O ambiente de simulação de robótica CARMEN (Carnegie Mellon Robot Navigation Toolkit) foi utilizado para realização de todos os experimentos computacionais considerando cinco instâncias de mapas geradas artificialmente com obstáculos. O desempenho do algoritmo desenvolvido, A*PSO, foi comparado com os algoritmos A*, PSO convencional e A* Estado Híbrido. A análise dos resultados indicou que o algoritmo A*PSO híbrido desenvolvido superou em qualidade de solução o PSO convencional. Apesar de ter encontrado melhores soluções em 40% das instâncias quando comparado com o A*, o A*PSO apresentou trajetórias com menos pontos de guinada. Investigando os resultados obtidos para o modelo não holonômico, o A*PSO obteve caminhos maiores entretanto mais suaves e seguros.
Autonomous robots with the ability of planning their own way is a challenge that attracts many researchers in the area of robot navigation. In this context, this work aims to implement a hybrid PSO algorithm for planning paths in static environments for holonomic and non-holonomic vehicles. The proposed algorithm has two phases: the first uses A* algorithm to generates an initial and feasible trajectory which is optimized by the PSO algorithm in the second stage. Finally a post path planning phase can be applied in order to adapt it to non-holonomic vehicle kinematic constraints. The Ackerman model has been considered for the experiments. The Carnegie Mellon Robot Navigation Toolkit (CARMEN) was used to perform the computational experiments considering five instances of maps artificially generated with obstacles. The performance of the A*PSO algorithm was compared with A*, PSO and A*-Hybrid State. The results of the dynamic instances were not compared with other algorithms. The computational results indicates that the algorithm A*PSO outperformes the PSO algorithm. With respect to the algorithm A*, the A*PSO achieved better solutions for 40% of the tested instances, but all of them, with less waypoints. For non-holonomic instances, the A*PSO obtained longer paths, however smoother and safer.

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Ryšánek, Martin. "Vícepásmová magnetická anténa". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218593.

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The thesis deals with a parametric analysis of a magnetic multiband antenna and explains the principle of its operation. In the thesis, an optimization of the antenna by the particle swarm optimization is performed in order to meet impedance matching in prescribed frequency bands.

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SIMONE, MARCO. "Optimization of microwave devices". Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266750.

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This thesis deals with the optimization techniques for the improvement of the microwave devices performance. In particular, the technique proposed considers the Particle Swarm Optimization algorithm and applies such an algorithm to different devices. Different techniques are developed to connect the optimization with an electromagnetic analysis tool. In the first method the algorithm has been connected to a numerical technique for the evaluation of the device performance (FDFD). The second technique consists on the integration of the algorithm with a 3D Simulation CAD (HFSS, CST). The microwave devices under test are a ridge waveguide (in different configurations), a resonant cavity, a waveguide impedance transformer and an electromagnetic band gap structure. Both the approaches result to be effective for the purpose even in the event that a constraint between conficting requirements is requested.

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Abuella, Mohamed A. "STUDY OF PARTICLE SWARM FOR OPTIMAL POWER FLOW IN IEEE BENCHMARK SYSTEMS INCLUDING WIND POWER GENERATORS". OpenSIUC, 2012. https://opensiuc.lib.siu.edu/theses/991.

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AN ABSTRACT OF THE THESIS OF Mohamed A. Abuella, for the Master of Science degree in Electrical and Computer Engineering, presented on May 10, 2012, at Southern Illinois University Carbondale. TITLE:STUDY OF PARTICLE SWARM FOR OPTIMAL POWER FLOW IN IEEE BENCHMARK SYSTEMS INCLUDING WIND POWER GENERATORS MAJOR PROFESSOR: Dr. C. Hatziadoniu, The aim of this thesis is the optimal economic dispatch of real power in systems that include wind power. The economic dispatch of wind power units is quite different of conventional thermal units. In addition, the consideration should take the intermittency nature of wind speed and operating constraints as well. Therefore, this thesis uses a model that considers the aforementioned considerations in addition to whether the utility owns wind turbines or not. The optimal power flow (OPF) is solved by using one of the modern optimization algorithms: the particle swarm optimization algorithm (PSO). IEEE 30-bus test system has been adapted to study the implementation PSO algorithm in OPF of conventional-thermal generators. A small and simple 6-bus system has been used to study OPF of a system that includes wind-powered generators besides to thermal generators. The analysis of investigations on power systems is presented in tabulated and illustrative methods to lead to clear conclusions.

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Haviar, Martin. "Optimalizace investičního portfolia pomocí metaheuristiky". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2015. http://www.nusl.cz/ntk/nusl-224904.

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This thesis deals with design and implementation of an investment model, which applies methods of Post-modern portfolio theory. Particle swarm optimization (PSO) metaheuristic was used for portfolio optimization and the parameters were analyzed with several experiments. Johnsons SU distribution was used for estimation of future returns as it proved to be the best of analyzed distributions. The result is software application written in Python, which is tested for stability and performance of model in extreme situations.

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Cai, Xinye. "A multi-objective GP-PSO hybrid algorithm for gene regulatory network modeling". Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1492.

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Abdelrasoul, Nader. "Optimization Techniques For an Artificial Potential Fields Racing Car Controller". Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-6211.

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Context. Building autonomous racing car controllers is a growing field of computer science which has been receiving great attention lately. An approach named Artificial Potential Fields (APF) is used widely as a path finding and obstacle avoidance approach in robotics and vehicle motion controlling systems. The use of APF results in a collision free path, it can also be used to achieve other goals such as overtaking and maneuverability. Objectives. The aim of this thesis is to build an autonomous racing car controller that can achieve good performance in terms of speed, time, and damage level. To fulfill our aim we need to achieve optimality in the controller choices because racing requires the highest possible performance. Also, we need to build the controller using algorithms that does not result in high computational overhead. Methods. We used Particle Swarm Optimization (PSO) in combination with APF to achieve optimal car controlling. The Open Racing Car Simulator (TORCS) was used as a testbed for the proposed controller, we have conducted two experiments with different configuration each time to test the performance of our APF- PSO controller. Results. The obtained results showed that using the APF-PSO controller resulted in good performance compared to top performing controllers. Also, the results showed that the use of PSO proved to enhance the performance compared to using APF only. High performance has been proven in the solo driving and in racing competitions, with the exception of an increased level of damage, however, the level of damage was not very high and did not result in a controller shut down. Conclusions. Based on the obtained results we have concluded that the use of PSO with APF results in high performance while taking low computational cost.

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Travessa, Sheila Santisi. "Uso de redes neurais artificiais como metamodelo na otimização por algoritmo PSO (particle swarm optimization') em problemas de mapeamento eletromagnético de ambientes". reponame:Repositório Institucional da UFSC, 2017. https://repositorio.ufsc.br/xmlui/handle/123456789/183229.

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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2017.
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Este trabalho se propõe a fazer uma análise de ferramentas de otimização e custo computacional através de um estudo de caso proposto por Grubisic (2012), que trata da otimização do posicionamento de antenas em sistemas de comunicação sem fio para ambientes interiores (indoor) por meio de meta-heurísticas populacionais associadas à Técnica de Traçado de Raios, em que algoritmos Genéticos (GA) e Otimizadores por Enxames de Partículas (PSO) foram as duas modalidades de meta-heurísticas utilizadas como ferramentas de otimização. A proposta desta tese baseou-se na utilização da técnica de traçado de raios quase 3D (RTQ3D) para produzir o valor dos campos eletromagnéticos iniciais e calcular a função de mérito (fitness) para 160 receptores de acordo com os possíveis posicionamentos de duas antenas a serem distribuídas no ambiente em questão. As variáveis do problema são compostas pelos valores dos campos magnéticos para os 160 receptores em função das posições das antenas das estações radiobase, que servem como dados de entrada para o algoritmo da Rede Neural Artificial, Perceptron multicamadas, com algoritmo de aprendizado backpropagation Real. Os valores dos campos magnéticos associados às posições das antenas por sua vez entram como valores a serem aprendidos pela rede, ou seja, o professor da RMLP. Após o aprendizado da Rede Neural Artificial, que é o metamodelo utilizado com o objetivo de realizar eficientemente os cálculos do otimizador, entra o otimizador por enxame de partículas (PSO) para efetuar o posicionamento ótimo das antenas com uma redução significativa no custo computacional. Por fim, um dos exemplos propostos por Grubisic (2012) foi implementado como estudo de caso desta pesquisa, utilizando essa nova estrutura de análise, PSO com RMLP, como metamodelo. Essa estrutura é bem recomendada para projetos eletromagnéticos, entretanto ainda não foi aplicada para esse tipo de análise. O objetivo principal seria a diminuição do custo computacional, que no caso em questão é bem significativo. Portanto, essa tese tem um caráter inédito em relação às ferramentas usadas e ao objetivo principal (redução do custo computacional).
Abstract : This research has proposed to do an analysis of optimization tools and computational cost using a case study proposed by Grubisic (2012), which addressed optimization of the antennas positioning in wireless communication systems for indoor environments through meta-population heuristics associated with ray tracing technique, in which algorithms Genetic (GA) and Optimizers for Swarms of particles (PSO) were the two types of meta-heuristics used as optimization tools. The purpose of this thesis was based on the use of almost 3D ray tracing technique (RTQ3D) to produce the value of the initial electromagnetic fields and calculating the merit function (fitness) to 160 receivers according to the possible placements of two antennas which are distributed in the environment in a matter. The problem variables consist of the values of the magnetic fields to the 160 receivers depending on the positions of the antennas of the access points, which serve as input data for the algorithm of Artificial Neural Network, multilayer perceptron with Real backpropagation learning algorithm. The problem variables consist of the values of 160 magnetic fields to 160 receivers on the basis of the positions of the antennas of the access points, which serve as input data for the algorithm of Artificial Neural Network, multilayer perceptron with backpropagation real learning algorithm. The values of the magnetic fields associated with the positions of the antennas in turn to input values to be learned by the network, or the teacher RMLP. After learning of Artificial Neural Network, which is the metamodel used in order to enable the calculation of the optimizer, with a lower computational cost, the optimizer particle swarm enters (PSO) to make the optimum positioning of the antennas with a significant reduction the computational cost. Finally, one of the examples proposed by Grubisic (2012) is implemented as a case study of this research using this new analysis structure, PSO using RMLP as metamodel. This structure is well recommended for electromagnetic designs, but has not been applied to this type of analysis. The main objective would be to reduce the computational cost, which in this case is significant. Therefore, this thesis has a unique character in relation to the tools used and the main objective (reducing the computational cost).

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Zemzami, Maria. "Variations sur PSO : approches parallèles, jeux de voisinages et applications Application d’un modèle parallèle de la méthode PSO au problème de transport d’électricité A modified Particle Swarm Optimization algorithm linking dynamic neighborhood topology to parallel computation An evolutionary hybrid algorithm for complex optimization problems Interoperability optimization using a modified PSO algorithm A comparative study of three new parallel models based on the PSO algorithm Optimization in collaborative information systems for an enhanced interoperability network". Thesis, Normandie, 2019. http://www.theses.fr/2019NORMIR11.

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Reconnue depuis de nombreuses années comme une méthode efficace pour la résolution de problèmes difficiles, la méta-heuristique d’optimisation par essaim de particules PSO (Particle Swarm Optimization) présente toutefois des inconvénients dont les plus étudiés sont le temps de calcul élevé et la convergence prématurée. Cette thèse met en exergue quelques variantes de la méthode PSO visant à échapper à ces deux inconvénients de la méthode. Ces variantes combinent deux approches : la parallélisation de la méthode de calcul et l’organisation de voisinages appropriés pour les particules. L’évaluation de la performance des modèles proposés a été effectuée sur la base d'une expérimentation sur une série de fonctions tests. A la lumière de l’analyse des résultats expérimentaux obtenus, nous observons que les différents modèles proposés donnent des résultats meilleurs que ceux du PSO classique en termes de qualité de la solution et du temps de calcul. Un modèle basé PSO a été retenu et développé en vue d'une expérimentation sur le problème du transport d’électricité. Une variante hybride de ce modèle avec la méthode du recuit simulé SA (Simulated Annealing) a été considérée et expérimentée sur la problématique des réseaux de collaboration
Known for many years as a stochastic metaheuristic effective in the resolution of difficult optimization problems, the Particle Swarm Optimization (PSO) method, however, shows some drawbacks, the most studied: high running time and premature convergence. In this thesis we consider some variants of the PSO method to escape these two disadvantages. These variants combine two approaches: the parallelization of the calculation and the organization of appropriate neighborhoods for the particles. To prove the performance of the proposed models, we performed an experiment on a series of test functions. By analyzing the obtained experimental results, we observe that the proposed models based on the PSO algorithm performed much better than basic PSO in terms of computing time and solution quality. A model based on the PSO algorithm was selected and developed for an experiment on the problem of electricity transmission. A hybrid variant of this model with Simulated Annealing (SA) algorithm has been considered and tested on the problem of collaborative networks

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Vignogna, Antoniangelo. "Swarm of Drones: il futuro delle tecnologie autonome". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20385/.

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Partendo dalla definizione di APR “aeromobile a pilotaggio remoto”, meglio conosciuto come DRONE, questo lavoro di tesi si propone di esaminare gli aspetti principali di tale tecnologia e di osservare come si è evoluta nel tempo, cominciando dalle origini fino ad arrivare alla nascita dei droni con intelligenza artificiale. Successivamente, viene posta l’attenzione sugli “Swarm of Drones”, droni autonomi capaci di volare in gruppo interagendo con l’ambiente circostante ma senza interferire tra di loro. Viene, poi, presentato il concetto di “Swarm Intelligence”, analizzando la sua precisione, affidabilità e vulnerabilità. A questo punto vengono esaminati i vari ambiti di utilizzo, partendo da quello civile e scientifico fino ad arrivare a quello militare e della sicurezza. A seguire, viene posta particolare attenzione sui futuri casi d’impiego che hanno già suscitato non poche polemiche e sugli aspetti etici che già dividono l’opinione pubblica, analizzano, infine, le possibili soluzioni per disciplinare e controllare tali tecnologie. Tra i risultati che questo lavoro di tesi ha portato alla luce spicca il fatto che opere di elevata tecnologia quali i droni emulino dei fattori biologici. Il loro sviluppo, infatti, ha avuto origine dallo studio delle dinamiche comportamentali degli insetti sociali. I ricercatori, tramite l’osservazione di questi fenomeni naturali sono riusciti a creare degli algoritmi per la risoluzione di problemi complessi che sono poi stati utilizzati per la creazione degli sciami di droni, spesso impiegati anche in azioni militari. Ovviamente, sono emersi anche i vantaggi e gli svantaggi di tali creazioni. Tra gli svantaggi emerge la costruzione e lo sviluppo delle armi autonome che fa porre l’attenzione sugli aspetti etici del loro utilizzo. Per quanto concerne i vantaggi, sicuramente l’utilizzo dei droni in campo medico potrà essere di vitale importanza per lo sviluppo di un innovativo sistema in grado di combattere le cellule tumorali.

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Husk, Evan. "Imitating individualized facial expressions in a human-like avatar through a hybrid particle swarm optimization - tabu search algorithm". Honors in the Major Thesis, University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/567.

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This thesis describes a machine learning method for automatically imitating a particular person's facial expressions in a human-like avatar through a hybrid Particle Swarm Optimization - Tabu Search algorithm. The muscular structures of the facial expressions are measured by Ekman and Friesen's Facial Action Coding System (FACS). Using a neutral face as a reference, the minute movements of the Action Units, used in FACS, are automatically tracked and mapped onto the avatar using a hybrid method. The hybrid algorithm is composed of Kennedy and Eberhart's Particle Swarm Optimization algorithm (PSO) and Glover's Tabu Search (TS). Distinguishable features portrayed on the avatar ensure a personalized, realistic imitation of the facial expressions. To evaluate the feasibility of using PSO-TS in this approach, a fundamental proof-of-concept test is employed on the system using the OGRE avatar. This method is analyzed in-depth to ensure its proper functionality and evaluate its performance compared to previous work.
B.S.P.E.
Bachelors
Engineering and Computer Science
Computer Engineering

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Papacostantis, Evangelos. "Competitive co-evolution of trend reversal indicators using particle swarm optimisation". Diss., University of Pretoria, 2010. http://hdl.handle.net/2263/23929.

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Computational Intelligence has found a challenging testbed for various paradigms in the financial sector. Extensive research has resulted in numerous financial applications using neural networks and evolutionary computation, mainly genetic algorithms and genetic programming. More recent advances in the field of computational intelligence have not yet been applied as extensively or have not become available in the public domain, due to the confidentiality requirements of financial institutions. This study investigates how co-evolution together with the combination of par- ticle swarm optimisation and neural networks could be used to discover competitive security trading agents that could enable the timing of buying and selling securities to maximise net profit and minimise risk over time. The investigated model attempts to identify security trend reversals with the help of technical analysis methodologies. Technical market indicators provide the necessary market data to the agents and reflect information such as supply, demand, momentum, volatility, trend, sentiment and retracement. All this is derived from the security price alone, which is one of the strengths of technical analysis and the reason for its use in this study. The model proposed in this thesis evolves trading strategies within a single pop- ulation of competing agents, where each agent is represented by a neural network. The population is governed by a competitive co-evolutionary particle swarm optimi- sation algorithm, with the objective of optimising the weights of the neural networks. A standard feed forward neural network architecture is used, which functions as a market trend reversal confidence. Ultimately, the neural network becomes an amal- gamation of the technical market indicators used as inputs, and hence is capable of detecting trend reversals. Timely trading actions are derived from the confidence output, by buying and short selling securities when the price is expected to rise or fall respectively. No expert trading knowledge is presented to the model, only the technical market indicator data. The co-evolutionary particle swarm optimisation model facilitates the discovery of favourable technical market indicator interpretations, starting with zero knowledge. A competitive fitness function is defined that allows the evaluation of each solution relative to other solutions, based on predefined performance metric objectives. The relative fitness function in this study considers net profit and the Sharpe ratio as a risk measure. For the purposes of this study, the stock prices of eight large market capitalisation companies were chosen. Two benchmarks were used to evaluate the discovered trading agents, consisting of a Bollinger Bands/Relative Strength Index rule-based strategy and the popular buy-and-hold strategy. The agents that were discovered from the proposed hybrid computational intelligence model outperformed both benchmarks by producing higher returns for in-sample and out-sample data at a low risk. This indicates that the introduced model is effective in finding favourable strategies, based on observed historical security price data. Transaction costs were considered in the evaluation of the computational intelligent agents, making this a feasible model for a real-world application. Copyright
Dissertation (MSc)--University of Pretoria, 2010.
Computer Science
unrestricted

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Röber, Marcel. "Multikriterielle Optimierungsverfahren für rechenzeitintensive technische Aufgabenstellungen". Master's thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-77679.

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Die Optimierung spielt in der Industrie und Technik eine entscheidende Rolle. Für einen Betrieb ist es beispielsweise äußerst wichtig, die zur Verfügung stehenden Ressourcen optimal zu nutzen und Betriebsabläufe effizient zu gestalten. Damit diese Vorhaben umgesetzt werden können, setzt man Methoden der Optimierung ein. Die Zielstellungen werden als eine abstrakte mathematische Aufgabe formuliert und anschließend wird versucht, dieses Problem mit einem Optimierungsverfahren zu lösen. Da die Komplexität der Problemstellungen in der Praxis ansteigt, sind exakte Verfahren in der Regel nicht mehr effizient anwendbar, sodass andere Methoden zum Lösen dieser Aufgaben entwickelt werden müssen, die in angemessener Zeit eine akzeptable Lösung finden. Solche Methoden werden als Approximationsalgorithmen bezeichnet. Im Gegensatz zu den exakten Verfahren ist der Verlauf der Optimierung bei dieser Verfahrensklasse vom Zufall abhängig. Dadurch lassen sich in der Regel keine Konvergenzaussagen beweisen. Dennoch hat sich gezeigt, dass Approximationsalgorithmen viel versprechende Ergebnisse für eine Vielzahl von unterschiedlichen Problemstellungen liefern. Zwei Approximationsalgorithmen werden in dieser Arbeit vorgestellt, untersucht und erweitert. Zum einen steht ein Verfahren im Vordergrund, welches aus Beobachtungen in der Natur entstanden ist. Es gibt Lebewesen, die durch verblüffend einfache Strategien in der Lage sind, komplexe Probleme zu lösen. Beispielsweise bilden Fische Schwärme, um sich vor Fressfeinden zu schützen. Der Fischschwarm kann dabei als selbstorganisierendes System verstanden werden, bei dem die Aktivitäten der einzelnen Fische hauptsächlich von den Bewegungen der Nachbarfische abhängig sind. An diesem erfolgreichen Schwarmverhalten ist der moderne Approximationsalgorithmus der Partikelschwarmoptimierung angelehnt. Weiterhin wird ein ersatzmodellgestütztes Verfahren präsentiert. Der Ausgangspunkt dieses Optimierungsverfahrens ist der Aufbau von Ersatzmodellen, um das Verhalten der Zielfunktionen anhand der bisherigen Auswertungen vorhersagen zu können. Damit so wenig wie möglich Funktionsauswertungen vorgenommen werden müssen, wird bei diesem Verfahren ein hoher Aufwand in die Wahl der Punkte investiert, welche auszuwerten sind. Die vorliegende Diplomarbeit gliedert sich wie folgt. Zunächst werden die mathematischen Grundlagen für das Verständnis der weiteren Ausführungen gelegt. Insbesondere werden multikriterielle Optimierungsaufgaben betrachtet und klassische Lösungsansätze aufgezeigt. Das dritte Kapitel beschäftigt sich mit der Partikelschwarmoptimierung. Dieser „naturanaloge Approximationsalgorithmus“ wird ausführlich dargelegt und analysiert. Dabei stehen die Funktionsweise und der Umgang mit mehreren Zielen und Restriktionen im Vordergrund der Ausarbeitung. Ein ersatzmodellgestütztes Optimierungsverfahren wird im Anschluss darauf vorgestellt und erweitert. Neben der Verfahrensanalyse, ist die Behebung der vorhandenen Schwachstellen ein vorrangiges Ziel dieser Untersuchung. Die eingeführten und implementierten Verfahren werden im fünften Kapitel an geeigneten analytischen und technischen Problemen verifiziert und mit anderen Approximationsalgorithmen verglichen. Anschließend werden Empfehlungen für die Verwendung der Verfahren gegeben. Die gewonnenen Kenntnisse werden im letzten Kapitel zusammengefasst und es wird ein Ausblick für zukünftige Forschungsthemen gegeben

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Banvait, Harpreetsingh. "OPTIMAL ENERGY MANAGEMENT SYSTEM OF PLUG-IN HYBRID ELECTRIC VEHICLE". ProQuest, 2009. http://hdl.handle.net/1805/2095.

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Indiana University-Purdue University Indianapolis (IUPUI)
Plug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They can store electrical energy from a domestic power supply and can drive the vehicle alone in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80 % of the American driving public on average drives under 50 miles per day. A PHEV vehicle that can drive up to 50 miles by making maximum use of cheaper electrical energy from a domestic supply can significantly reduce the conventional fuel consumption. This may also help in improving the environment as PHEVs emit less harmful gases. However, the Energy Management System (EMS) of PHEVs would have to be very different from existing EMSs of HEVs. In this thesis, three different Energy Management Systems have been designed specifically for PHEVs using simulated study. For most of the EMS development mathematical vehicle models for powersplit drivetrain configuration are built and later on the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The main objective of the study is to design EMSs to reduce fuel consumption by the vehicle. These EMSs are compared with existing EMSs which show overall improvement. x In this thesis the final EMS is designed in three intermediate steps. First, a simple rule based EMS was designed to improve the fuel economy for parametric study. Second, an optimized EMS was designed with the main objective to improve fuel economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to obtain the optimum parameter values. This EMS has provided optimum parameters which result in optimum blended mode operation of the vehicle. Finally, to obtain optimum charge depletion and charge sustaining mode operation of the vehicle an advanced PSO EMS is designed which provides optimal results for the vehicle to operate in charge depletion and charge sustaining modes. Furthermore, to implement the developed advanced PSO EMS in real-time a possible real time implementation technique is designed using neural networks. This neural network implementation provides sub-optimal results as compared to advanced PSO EMS results but it can be implemented in real time in a vehicle. These EMSs can be used to obtain optimal results for the vehicle driving conditions such that fuel economy is improved. Moreover, the optimal designed EMS can also be implemented in real-time using the neural network procedure described.

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Carvalho, Érica da Costa Reis. "Solução de problemas de otimização com restrições usando estratégias de penalização adaptativa e um algoritmo do tipo PSO". Universidade Federal de Juiz de Fora (UFJF), 2014. https://repositorio.ufjf.br/jspui/handle/ufjf/3506.

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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Nos últimos anos, várias meta-heurísticas têm sido adotadas para a solução de problemas de otimização com restrições. Uma dessas meta-heurísticas que se torna cada vez mais popular é a Otimização por Enxame de Partículas (Particle Swarm Optimization - PSO). O PSO é baseado na metáfora de como algumas espécies compartilham informações e, em seguida, usam essas informações para mover-se até os locais onde os alimentos estão localizados. A população é formada por um conjunto de indivíduos denominado partículas que representa possíveis soluções dentro de um espaço de busca multidimensinal. Neste trabalho, são analisados problemas clássicos de otimização com restrições onde um algoritmo PSO os trata como sendo sem restrições através da introdução de um método de penalização adaptativa (Adaptive Penalty Method - APM). O APM adapta o valor dos coeficientes de penalização de cada restrição fazendo uso de informações coletadas da população, tais como a média da função objetivo e o nível de violação de cada restrição. Diversos experimentos computacionais são realizados visando avaliar o desempenho do algoritmo considerando vários problemas testes encontrados na literatura.
In recent years, several meta-heuristics have been adopted for the solution of constrained optimization problems. One of these meta-heuristic that is becoming increasingly popular is the Particle Swarm Optimization - PSO. PSO is based on the metaphor of how some species share information and then use this information to move to the places where food is located. The population is formed by a group of individuals called particles representing possible solutions within a space multidimensional search. In this thesis, classical problems of constrained optimization where a PSO algorithm treats them as being unconstrained by introducing a method of adaptive penalty (Adaptive Penalty Method - APM) are analyzed. The APM adjusts the value of the penalty coeffcients of each constraint using the information collected from the population, such as the average of the objective function as well as the level of violation of each constraint. Several computational experiments are conducted to assess the performance the algorithm tests considering various problems found in the literature.

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Sheikhi, Mehrabadi Elham. "Multi-Objective Optimization of Combined Heat and Power Industrial Microgrid". Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/21868.

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With the advent of distributed energy resources (DERs), such as solar photovoltaic (PV), distributed storages (DSs), and combined heat and power (CHP) generators, an opportunity was given to various customers to be satisfied with their local electricity and heat providers. Microgrid is a small-scale, localized power network that encompasses DERs, loads, and controllers. Microgrids could be a promising alternative for the conventional power system to solve environmental and economic concerns. Recently, CHP-based microgrids have widely attracted attention due to the cost effectiveness and pollution reduction. Most of the recent studies attempted to simplify microgrid components, loads, and constraints to decrease the analysis complexity; however, in this work, a comprehensive study of an industrial microgrid (IMG) comprising natural gas-based CHPs with different generation limits, PVs with intermittent outputs, PV storages, and boilers is taken into the consideration to totally satisfy variant electrical and heat demand over 24-hour period. Emission and cost are conflicting concerns in microgrid generation scheduling optimization and simultaneous optimization of these objectives was challenging. Optimization process is viable using various algorithms and techniques. Among all different approaches, NSGA-II and PSO are among the top-rated algorithms. In this innovative study, NSGA-II and PSO have been developed to reduce cost and emission objectives simultaneously with applying the most critical constraints and variable loads. The practical ability of the optimization process using the applied algorithms are demonstrated by analyzing two scenarios at the presence and absence of PV systems. Although both methods could satisfy electrical and thermal demands, the NSGA-II presented an improved solution compared to PSO method. Also, PV system effect on cost and pollution reduction was significant.

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Helbig, Marde. "Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation". Thesis, University of Pretoria, 2012. http://hdl.handle.net/2263/28161.

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Most optimisation problems in everyday life are not static in nature, have multiple objectives and at least two of the objectives are in conflict with one another. However, most research focusses on either static multi-objective optimisation (MOO) or dynamic singleobjective optimisation (DSOO). Furthermore, most research on dynamic multi-objective optimisation (DMOO) focusses on evolutionary algorithms (EAs) and only a few particle swarm optimisation (PSO) algorithms exist. This thesis proposes a multi-swarm PSO algorithm, dynamic Vector Evaluated Particle Swarm Optimisation (DVEPSO), to solve dynamic multi-objective optimisation problems (DMOOPs). In order to determine whether an algorithm solves DMOO efficiently, functions are required that resembles real world DMOOPs, called benchmark functions, as well as functions that quantify the performance of the algorithm, called performance measures. However, one major problem in the field of DMOO is a lack of standard benchmark functions and performance measures. To address this problem, an overview is provided from the current literature and shortcomings of current DMOO benchmark functions and performance measures are discussed. In addition, new DMOOPs are introduced to address the identified shortcomings of current benchmark functions. Guides guide the optimisation process of DVEPSO. Therefore, various guide update approaches are investigated. Furthermore, a sensitivity analysis of DVEPSO is conducted to determine the influence of various parameters on the performance of DVEPSO. The investigated parameters include approaches to manage boundary constraint violations, approaches to share knowledge between the sub-swarms and responses to changes in the environment that are applied to either the particles of the sub-swarms or the non-dominated solutions stored in the archive. From these experiments the best DVEPSO configuration is determined and compared against four state-of-the-art DMOO algorithms.
Thesis (PhD)--University of Pretoria, 2012.
Computer Science
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CAVALCANTI, Leonardo Machado. "Aplicativo web para projeto de sensores ópticos baseados em ressonância de plasmons de superífice em interfaces planares". Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/18272.

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CNPQ
Um dos principais desafios no projeto de sensores baseados em Ressonância de Plasmons de Superfície — RPS — é maximizar sua sensibilidade. Neste trabalho é proposto o uso de dois algoritmos heurísticos, Monte Carlo e Enxame de Partículas, para otimização de sensores baseados em RPS em interfaces planares, i.e, nas configurações de Kretschmann e de Otto, sem o auxílio da aproximação lorentziana para a curva de ressonância. Devido à natureza probabilística dos algoritmos, consegue-se obter um método simples e robusto para atingir essa otimização. É feita uma comparação quanto à eficiência computacional dos algoritmos em relação ao método tradicional de otimização, ficando demonstrado que o método de Enxame de Partículas é o mais eficiente em relação às outras técnicas. Com o emprego desse método, a dependência espectral dos parâmetros ótimos é obtida para sensores utilizando vários metais nas configurações de Kretschmann e de Otto, tanto para aplicações em meios gasosos quanto em meios aquosos. Um aplicativo foi desenvolvido e sua funcionalidade demonstrada, que pode ser executado diretamente via web, com base na metodologia proposta, para otimização de sensores RPS em interfaces planares.
One of the main challenges in the design of surface plasmon resonance – SPR – sensor systems is to maximize their sensitivity. In this work one proposes the use of two heuristic algorithms, Monte Carlo and Particle Swarm, for optimization of SPR sensors in planar interfaces, i.e, in the Kretschmann and Otto configurations, without use of the Lorentzian approximation to the resonance curve. Because of the probabilistic nature of the algorithms, one manages to obtain a simple and robust method to achieve optimization. A comparison is made on the computational efficiency of the algorithm relative to the traditional method of optimization, showing that the particle swarm optimization method is more efficient compared to other techniques. By employing this method, the spectral dependence of optimum parameters is obtained for sensors using a wide range of metal films in the Kretschmann and Otto configurations, both for applications in gaseous an in aqueous media. An app was developed and its functionality can be demonstrated, by direct execution via web, based on the proposed methodology for optimization of SPR sensors on planar interfaces.

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Moravec, Prokop. "Shape Optimization of the Hydraulic Machine Flow Passages". Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433572.

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Paiva, Rafael Cortes de. "Utilização de CPGs e técnicas de inteligência computacional na geração de marcha em robôs humanóides". reponame:Repositório Institucional da UnB, 2014. http://repositorio.unb.br/handle/10482/17048.

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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2014.
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Nesse trabalho foi realizado o estudo de técnicas bio-inspiradas para gerar a marcha de um robô bípede. Foi utilizado o conceito de CPG, Central Pattern Generator (CPG), que é uma rede neural capaz de produzir respostas rítmicas. Elas foram modeladas como osciladores acoplados chamados de osciladores neurais. Para tanto foram utilizados alguns modelos de osciladores, o modelo de Matsuoka, o modelo de Kuramoto e o modelo de Kuramoto com acoplamento entre a dinâmica do oscilador e a dinâmica da marcha. Foram usados dois modelos de robôs, o Bioloid e o NAO. Para otimizar os parâmetros dos osciladores foram utilizados o Algoritmo Genético (AG), o Particle Swarm Optimization (PSO) e o Nondominated sorting Genetic Algorithm II (NSGA-II). Foi utilizada uma função de custo que através de determinadas condições tem como objetivo obter uma marcha eficiente. No NSGA-II, além dessa função de custo, foi utilizada outra função de custo que considera o trabalho realizado pelo robô. Além disso, também foi utilizada a aprendizagem por reforço para treinar um controlador que corrige a postura do robô durante a marcha. Foi possível propor um framework para obter os parâmetros dos osciladores e através dele obter uma marcha estável em ambas as plataformas. Também foi possível propor um framework utilizando aprendizagem por reforço para treinar um controlador para corrigir a postura do robô com a marcha sendo gerado pelo oscilador de Kuramoto com acoplamento. O objetivo do algoritmo foi minimizar a velocidade do ângulo de arfagem do corpo do robô, dessa forma, a variação do ângulo de arfagem também foi minimizada consequentemente. Além disso, o robô andou mais “cautelosamente” para poder manter a postura e dessa forma percorreu uma distância menor do que se estivesse sem o controlador. ______________________________________________________________________________ ABSTRACT
This document describes computational optimized bipedal robot gait generators. Thegaits are applied by a neural oscillator, composed of coupled central pattern generators(CPG), which are neural networks capable of producing rhythmic output. The models ofthe oscillators used were the Matsuoka model, Kuramoto model and Kura moto model withcoupling between the dynamics of the oscillator and dynamics of the gait. Two bipedalrobots, a NAO and a Bioloid, were used. The neural oscillators were optimized with threealgorithms, a Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Nondominatedsorting Genetic Algorithm II (NSGA-II). It was used a fitness function that has theobjective to obtain an efficient gait through some conditions. In NSGA-II, besides this fitnessfunction, another one was used that has the objective to minimize the work done by therobot. Additionally, reinforcement learning techniques were used to train a controller thatcorrects the robots gait posture. It was proposed a framework to obtain the parameters of theoscillators used and obtain efficient gaits in both robots. Also, it was proposed a frameworkusing reinforcement learning to train a controller to correct the robots gait posture. The objective of the algorithm was to minimize the pitch angular velocity, consequently the pitchangle standard deviation was minimized. Additionally, the robot moved with more “caution” and walked less compared with the walk without the posture controller.

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Giratá, Daniel Ricardo Ojeda. "Metodologia de estimação dos parâmetros de um módulo termoelétrico baseada na implementação do algoritmo PSO". reponame:Repositório Institucional da UFABC, 2016.

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Orientador: Prof. Dr. Luiz A. Luz de Almeida
Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia Elétrica, 2016.
Modulos termoeletricos (TEM-Thermoelectric Modules) sao utilizados na geraçao de energia eletrica e na construcao de camaras termicas para caracterizacao de materiais como ligas de memoria de forma (SMA-Smart Memory Allow), dentre outros. Para ter uma correta representacao do TEM e necessaria a criaçao de um modelo matematico que consiga representar o seu funcionamento, tanto em corrente cont'ýnua como em demais frequ¿encias relevantes. No presente trabalho 'e proposto um modelo para a representa¸c¿ao de uma c¿amera t'ermica constru'ýda a partir de dois TEM, considerando-se as n¿ao-linearidades destes. M'etodos cl'assicos de estima¸c¿ao para modelos lineares nos par¿ametros n¿ao se aplicam para o modelo proposto. Para obten¸c¿ao dos valores dos par¿ametros do TEM, este 'e excitado com um sinal aleat'orio de multi-n'ývel (PRBS-Pseudo Random Binary Sequence) e a resposta 'e utilizada para o m'etodo n¿ao determin'ýstico do algoritmo de otimiza¸c¿ao, baseada no enxame de part'ýculas (PSO-Particle Swarm Optimization) fazer a estima¸c¿ao. O modelo escolhido para a caracteriza¸c¿ao da c¿amara t'ermica 'e n¿ao-linear. Este cont'em os par¿ametros t'ermicos din¿amicos, tais como: a camada superior, a placa superior, camada central, placa inferior e o dissipador de calor de cada um dos TEM, sendo no total 21 par¿ametros calculados pelo algoritmo PSO. O sinal de excita¸c¿ao consiste em um ru'ýdo branco que 'e antes filtrado, resultando em um sinal dinamicamente persistente, de tal forma que o TEM seja bem caracterizado. Resultados de simula¸c¿oes mostram a efetividade do algoritmo PSO na estima¸c¿ao de par¿ametros do modelo.
Thermoelectric Modules (TEM) are used in the power generation and construction of thermal cameras for material characterization such as Smart Memory Allow (SMA), among other. In order to obtain a correct TEM representation, it is necessary a proper model identification procedure to represent the TEM operation, both in D.C. and other relevant frequencies. In this paper, a TEM model is proposed, for the representation of a thermal camera built from two TEM. TEM non linear characteristics were considered. Classical methods for linear parameters estimation are not apply to the proposed model. To obtain the TEM parameters, it power density of a white noise, and then is used the temperature response for the Particle Swarm Optimization algorithm (PSO) to make the estimation. The chosen model is nonlinear with 21 parameters, wich represent the TEM: the top layer, the hot side, the middle layer, cold side and the heatsink. For numerical stability, the white noise excitation is filtered before, geting a dynamically persistent signal, so TEM will be properly characterized. Simulation results show the effectiveness of the PSO in TEM parameters estimation.

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Saeed, Nagham. "Intelligent MANET optimisation system". Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5674.

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In the literature, various Mobile Ad hoc NETwork (MANET) routing protocols proposed. Each performs the best under specific context conditions, for example under high mobility or less volatile topologies. In existing MANET, the degradation in the routing protocol performance is always associated with changes in the network context. To date, no MANET routing protocol is able to produce optimal performance under all possible conditions. The core aim of this thesis is to solve the routing problem in mobile Ad hoc networks by introducing an optimum system that is in charge of the selection of the running routing protocol at all times, the system proposed in this thesis aims to address the degradation mentioned above. This optimisation system is a novel approach that can cope with the network performance’s degradation problem by switching to other routing protocol. The optimisation system proposed for MANET in this thesis adaptively selects the best routing protocol using an Artificial Intelligence mechanism according to the network context. In this thesis, MANET modelling helps in understanding the network performance through different contexts, as well as the models’ support to the optimisation system. Therefore, one of the main contributions of this thesis is the utilisation and comparison of various modelling techniques to create representative MANET performance models. Moreover, the proposed system uses an optimisation method to select the optimal communication routing protocol for the network context. Therefore, to build the proposed system, different optimisation techniques were utilised and compared to identify the best optimisation technique for the MANET intelligent system, which is also an important contribution of this thesis. The parameters selected to describe the network context were the network size and average mobility. The proposed system then functions by varying the routing mechanism with the time to keep the network performance at the best level. The selected protocol has been shown to produce a combination of: higher throughput, lower delay, fewer retransmission attempts, less data drop, and lower load, and was thus chosen on this basis. Validation test results indicate that the identified protocol can achieve both a better network performance quality than other routing protocols and a minimum cost function of 4.4%. The Ad hoc On Demand Distance Vector (AODV) protocol comes in second with a cost minimisation function of 27.5%, and the Optimised Link State Routing (OLSR) algorithm comes in third with a cost minimisation function of 29.8%. Finally, The Dynamic Source Routing (DSR) algorithm comes in last with a cost minimisation function of 38.3%.

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del, Valle Yamille E. "Optimization of power system performance using facts devices". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29612.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Dr. Ronald G. Harley; Committee Member: Dr. Bonnie Heck; Committee Member: Dr. Deepak Divan; Committee Member: Dr. Ganesh K. Venayagamoorthy; Committee Member: Dr. Miroslav Begovic. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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