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Опубліковано: 11 вересня 2023

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Статті в журналах з теми "PSO (PRATICLE SWARM OPTIMIZATION)"

Aziz, Nor Azlina Ab, Zuwairie Ibrahim, Marizan Mubin, Sophan Wahyudi Nawawi, and Nor Hidayati Abdul Aziz. "Transitional Particle Swarm Optimization." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 3 (June 1, 2017): 1611. http://dx.doi.org/10.11591/ijece.v7i3.pp1611-1619.

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A new variation of particle swarm optimization (PSO) termed as transitional PSO (T-PSO) is proposed here. T-PSO attempts to improve PSO via its iteration strategy. Traditionally, PSO adopts either the synchronous or the asynchronous iteration strategy. Both of these iteration strategies have their own strengths and weaknesses. The synchronous strategy has reputation of better exploitation while asynchronous strategy is stronger in exploration. The particles of T-PSO start with asynchronous update to encourage more exploration at the start of the search. If no better solution is found for a number of iteration, the iteration strategy is changed to synchronous update to allow fine tuning by the particles. The results show that T-PSO is ranked better than the traditional PSOs.

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Golubovic, Ruzica, and Dragan Olcan. "Antenna optimization using Particle Swarm Optimization algorithm." Journal of Automatic Control 16, no. 1 (2006): 21–24. http://dx.doi.org/10.2298/jac0601021g.

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We present the results for two different antenna optimization problems that are found using the Particle Swarm Optimization (PSO) algorithm. The first problem is finding the maximal forward gain of a Yagi antenna. The second problem is finding the optimal feeding of a broadside antenna array. The optimization problems have 6 and 20 optimization variables, respectively. The preferred values of the parameters of the PSO algorithm are found for presented problems. The results show that the preferred parameters of PSO are somewhat different for optimization problems with different number of dimensions of the optimization space. The results that are found using the PSO algorithm are compared with the results that are found using other optimization algorithms, in order to estimate the efficiency of the PSO.

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Jiang, Chang Yuan, Shu Guang Zhao, Li Zheng Guo, and Chuan Ji. "An Improved Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 195-196 (August 2012): 1060–65. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.1060.

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Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented. Convergence condition of PSO is obtained through solving and analyzing the differential equation. By the experiments of four Benchmark function, the results show the performance of S-PSO improved more clearly than the standard PSO and random inertia weight PSO. Theoretical analysis and simulation experiments show that the S-PSO is efficient and feasible.

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Shen, Yuanxia, Linna Wei, Chuanhua Zeng, and Jian Chen. "Particle Swarm Optimization with Double Learning Patterns." Computational Intelligence and Neuroscience 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/6510303.

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Анотація:

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.

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Xu, Yu Fa, Jie Gao, Guo Chu Chen, and Jin Shou Yu. "Quantum Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 63-64 (June 2011): 106–10. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.106.

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Based on the problem of traditional particle swarm optimization (PSO) easily trapping into local optima, quantum theory is introduced into PSO to strengthen particles’ diversities and avoid the premature convergence effectively. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than PSO.

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Moraglio, Alberto, Cecilia Di Chio, Julian Togelius, and Riccardo Poli. "Geometric Particle Swarm Optimization." Journal of Artificial Evolution and Applications 2008 (February 21, 2008): 1–14. http://dx.doi.org/10.1155/2008/143624.

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Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimisation (PSO) and evolutionary algorithms. This connection enables us to generalise PSO to virtually any solution representation in a natural and straightforward way. The new Geometric PSO (GPSO) applies naturally to both continuous and combinatorial spaces. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces and report extensive experimental results. We also demonstrate the applicability of GPSO to more challenging combinatorial spaces. The Sudoku puzzle is a perfect candidate to test new algorithmic ideas because it is entertaining and instructive as well as being a nontrivial constrained combinatorial problem. We apply GPSO to solve the Sudoku puzzle.

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Zhang, Guan Yu, Xiao Ming Wang, Rui Guo, and Guo Qiang Wang. "An Improved Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 394 (September 2013): 505–8. http://dx.doi.org/10.4028/www.scientific.net/amm.394.505.

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This paper presents an improved particle swarm optimization (PSO) algorithm based on genetic algorithm (GA) and Tabu algorithm. The improved PSO algorithm adds the characteristics of genetic, mutation, and tabu search into the standard PSO to help it overcome the weaknesses of falling into the local optimum and avoids the repeat of the optimum path. By contrasting the improved and standard PSO algorithms through testing classic functions, the improved PSO is found to have better global search characteristics.

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Hudaib, Amjad A., and Ahmad Kamel AL Hwaitat. "Movement Particle Swarm Optimization Algorithm." Modern Applied Science 12, no. 1 (December 31, 2017): 148. http://dx.doi.org/10.5539/mas.v12n1p148.

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Particle Swarm Optimization (PSO) ia a will known meta-heuristic that has been used in many applications for solving optimization problems. But it has some problems such as local minima. In this paper proposed a optimization algorithm called Movement Particle Swarm Optimization (MPSO) that enhances the behavior of PSO by using a random movement function to search for more points in the search space. The meta-heuristic has been experimented over 23 benchmark faction compared with state of the art algorithms: Multi-Verse Optimizer (MFO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO) and particle Swarm Optimization (PSO). The Results showed that the proposed algorithm has enhanced the PSO over the tested benchmarked functions.

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Gonsalves, Tad, and Akira Egashira. "Parallel Swarms Oriented Particle Swarm Optimization." Applied Computational Intelligence and Soft Computing 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/756719.

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The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper, we propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of a multistage and a single stage of evolution. In the multi-stage of evolution, individual subswarms evolve independently in parallel, and in the single stage of evolution, the sub-swarms exchange information to search for the global-best. The two interweaved stages of evolution demonstrate better performance on test functions, especially of higher dimensions. The attractive feature of the PSO-PSO version of the algorithm is that it does not introduce any new parameters to improve its convergence performance. The strategy maintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO.

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Ma, Zi Rui. "Particle Swarm Optimization Based on Multiobjective Optimization." Applied Mechanics and Materials 263-266 (December 2012): 2146–49. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2146.

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PSO will population each individual as the search space without a volume and quality of particle. These particles in the search space at a certain speed flight, the speed according to its own flight experience and the entire population of flight experience dynamic adjustment. We describe the standard PSO, multi-objective optimization and MOPSO. The main focus of this thesis is several PSO algorithms which are introduced in detail and studied. MOPSO algorithm introduced adaptive grid mechanism of the external population, not only to groups of particle on variation, but also to the value scope of the particles and variation, and the variation scale and population evolution algebra in proportion.

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Дисертації з теми "PSO (PRATICLE SWARM OPTIMIZATION)"

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., and 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
Unrestricted

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Amiri, Mohammad Reza Shams, and 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
Unrestricted

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Книги з теми "PSO (PRATICLE SWARM OPTIMIZATION)"

López, Javier. Optimización multi-objetivo. Editorial de la Universidad Nacional de La Plata (EDULP), 2015. http://dx.doi.org/10.35537/10915/45214.

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Анотація:

Cuando hablamos de optimización en el ámbito de las ciencias de la computación hacemos referencia al mismo concepto coloquial asociado a esa palabra, la concreción de un objetivo utilizando la menor cantidad de recursos disponibles, o en una visión similar, la obtención del mejor objetivo posible utilizando todos los recursos con lo que se cuenta. Los métodos para encontrar la mejor solución (óptima) varían de acuerdo a la complejidad del problema enfrentado. Para problemas triviales, el cerebro humano posee la capacidad de resolverlos (encontrar la mejor solución) directamente, pero a medida que aumenta la complejidad del problema, se hace necesario contar con herramientas adicionales. En esta dirección, existe una amplia variedad de técnicas para resolver problemas complejos. Dentro de estas técnicas, podemos mencionar las técnicas exactas. Este tipo de algoritmos son capaces de encontrar las soluciones óptimas a un problema dado en una cantidad finita de tiempo. Como contrapartida, requiere que el problema a resolver cumpla con condiciones bastante restrictivas. Existen además un conjunto muy amplio de técnica aproximadas, conocidas como metaheurísticas. Estas técnicas se caracterizan por integrar de diversas maneras procedimientos de mejora local y estrategias de alto nivel para crear un proceso capaz de escapar de óptimos locales y realizar una búsqueda robusta en el espacio de búsqueda del problema. En su evolución, estos métodos han incorporado diferentes estrategias para evitar la convergencia a óptimos locales, especialmente en espacios de búsqueda complejos. Este tipo de procedimientos tienen como principal característica que son aplicables a cualquier tipo de problemas, sin requerir ninguna condición particular a cumplir por los mismos. Estas técnicas no garantizan en ningún caso la obtención de los valores óptimos de los problemas en cuestión, pero se ha demostrado que son capaces de alcanzar muy buenos valores de soluciones en períodos de tiempo cortos. Además, es posible aplicarlas a problemas de diferentes tipos sin mayores modificaciones, mostrando su robustez y su amplio espectro de uso. La mayoría de estas técnicas están inspiradas en procesos biológicos y/o físicos, y tratan de simular el comportamiento propio de estos procesos que favorecen la búsqueda y detección de soluciones mejores en forma iterativa. La más difundida de estas técnicas son los algoritmos genéticos, basados en el mecanismo de evolución natural de las especies. Existen diferentes tipos de problemas, y multitud de taxonomías para clasificar los mismos. En el alcance de este trabajo nos interesa diferenciar los problemas en cuanto a la cantidad de objetivos a optimizar. Con esta consideración en mente, surge una primera clasificación evidente, los problemas mono-objetivo, donde existe solo una función objetivo a optimizar, y los problemas multi-objetivo donde existe más de una función objetivo. En el presente trabajo se estudia la utilización de metaheurísticas evolutivas para la resolución de problemas complejos, con uno y con más de un objetivo. Se efectúa un análisis del estado de situación en la materia, y se proponen nuevas variantes de algoritmos existentes, validando que las mismas mejoran resultados reportados en la literatura. En una primera instancia, se propone una mejora a la versión canónica y mono-objetivo del algoritmo PSO, luego de un estudio detallado del patrón de movimientos de las partículas en el espacio de soluciones. Estas mejoras se proponen en las versiones de PSO para espacios continuos y para espacios binarios. Asimismo, se analiza la implementación de una versión paralela de esta técnica evolutiva. Como segunda contribución, se plantea una nueva versión de un algoritmo PSO multiobjetivo (MOPSO Multi Objective Particle Swarm Optimization) incorporando la posibilidad de variar dinámicamente el tamaño de la población, lo que constituye una contribución innovadora en problemas con mas de una función objetivo. Por último, se utilizan las técnicas representativas del estado del arte en optimización multi-objetivo aplicando estos métodos a la problemática de una empresa de emergencias médicas y atención de consultas domiciliarias. Se logró poner en marcha un proceso de asignación de móviles a prestaciones médicas basado en metaheurísticas, logrando optimizar el proceso de asignación de móviles médicos a prestaciones médicas en la principal compañía de esta industria a nivel nacional.

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Частини книг з теми "PSO (PRATICLE SWARM OPTIMIZATION)"

Wang, Feng-Sheng, and Li-Hsunan Chen. "Particle Swarm Optimization (PSO)." In Encyclopedia of Systems Biology, 1649–50. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_416.

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Badar, Altaf Q. H. "Different Applications of PSO." In Applying Particle Swarm Optimization, 191–208. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_11.

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Cuevas, Erik, and Alma Rodríguez. "Particle Swarm Optimization (PSO) Algorithm." In Metaheuristic Computation with MATLAB®, 159–81. First edition. | Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003006312-6.

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Couceiro, Micael, and Pedram Ghamisi. "Fractional-Order Darwinian PSO." In Fractional Order Darwinian Particle Swarm Optimization, 11–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19635-0_2.

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Ehteram, Mohammad, Akram Seifi, and Fatemeh Barzegari Banadkooki. "Structure of Particle Swarm Optimization (PSO)." In Application of Machine Learning Models in Agricultural and Meteorological Sciences, 23–32. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9733-4_2.

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Kao, Yucheng, Ming-Hsien Chen, and Kai-Ming Hsieh. "Combining PSO and FCM for Dynamic Fuzzy Clustering Problems." In Swarm Intelligence Based Optimization, 1–8. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12970-9_1.

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Fernández-Brillet, Lucas, Oscar Álvarez, and Juan Luis Fernández-Martínez. "The PSO Family: Application to the Portfolio Optimization Problem." In Applying Particle Swarm Optimization, 111–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_7.

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Yarat, Serhat, Sibel Senan, and Zeynep Orman. "A Comparative Study on PSO with Other Metaheuristic Methods." In Applying Particle Swarm Optimization, 49–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_4.

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Deroussi, Laurent. "A Hybrid PSO Applied to the Flexible Job Shop with Transport." In Swarm Intelligence Based Optimization, 115–22. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12970-9_13.

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Gkaidatzis, Paschalis A., Aggelos S. Bouhouras, and Dimitris P. Labridis. "Application of PSO in Distribution Power Systems: Operation and Planning Optimization." In Applying Particle Swarm Optimization, 321–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_17.

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Тези доповідей конференцій з теми "PSO (PRATICLE SWARM OPTIMIZATION)"

Hu, Jhen-Jai, Yu-Te Su, and Tzuu-Hseng S. Li. "A novel ecological-biological-behavior praticle swarm optimization for Ackley's function." In 2010 International Symposium on Computer, Communication, Control and Automation (3CA). IEEE, 2010. http://dx.doi.org/10.1109/3ca.2010.5533436.

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Das, M. Taylan, L. Canan Dulger, and G. Sena Das. "Robotic applications with Particle Swarm Optimization (PSO)." In 2013 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2013. http://dx.doi.org/10.1109/codit.2013.6689537.

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Schutze, Oliver, El-ghazali Talbi, Gregorio Toscano Pulido, Carlos Coello Coello, and Luis Vicente Santana-Quintero. "A Memetic PSO Algorithm for Scalar Optimization Problems." In 2007 IEEE Swarm Intelligence Symposium. IEEE, 2007. http://dx.doi.org/10.1109/sis.2007.368036.

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Vatankhah, Ramin, Shahram Etemadi, Mohammad Honarvar, Aria Alasty, Mehrdad Boroushaki, and Gholamreza Vossoughi. "Online velocity optimization of robotic swarm flocking using particle swarm optimization (PSO) method." In 2009 6th International Symposium on Mechatronics and its Applications (ISMA). IEEE, 2009. http://dx.doi.org/10.1109/isma.2009.5164776.

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Pappala, V. S., and I. Erlich. "Power system optimization under uncertainties: A PSO approach." In 2008 IEEE Swarm Intelligence Symposium (SIS). IEEE, 2008. http://dx.doi.org/10.1109/sis.2008.4668276.

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Gies, D., and Y. Rahmat-Samii. "Particle swarm optimization (PSO) for reflector antenna shaping." In IEEE Antennas and Propagation Society Symposium, 2004. IEEE, 2004. http://dx.doi.org/10.1109/aps.2004.1331828.

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Kohler, Manoela, Leonardo Forero, Marley Vellasco, Ricardo Tanscheit, and Marco Aurelio Pacheco. "PSO+: A nonlinear constraints-handling particle swarm optimization." In 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016. http://dx.doi.org/10.1109/cec.2016.7744102.

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Ahmadie, Beryl Labique, Wanda Athira Luqyana, Wayan Firdaus Mahmudy, and Rio Arifando. "Milkfish Feed Optimization Using Adaptive Particle Swarm Optimization (PSO) Algorithm." In 2019 International Conference on Sustainable Information Engineering and Technology (SIET). IEEE, 2019. http://dx.doi.org/10.1109/siet48054.2019.8986094.

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Daneshyari, Moayed, and Gary G. Yen. "Solving constrained optimization using multiple swarm cultural PSO with inter-swarm communication." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586103.

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Wu, Di, and G. Gary Wang. "Enhanced Particle Swarm Optimization via Reinforcement Learning." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22519.

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Анотація:

Abstract Particle swarm optimization (PSO) method is a well-known optimization algorithm, which shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this paper, a reinforcement learning method is used to enhance PSO in convergence by replacing the uniformly distributed random number in the updating function by a random number generated from a well-selected normal distribution. The mean and variance of the normal distribution are estimated from the current state of each individual through a policy net. The historic behavior of the swarm group is learned to update the policy net and guide the selection of parameters of the normal distribution. The proposed algorithm is tested with numerical test functions and the results show that the convergence rate of PSO can be improved with the proposed Reinforcement Learning method (RL-PSO).

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Звіти організацій з теми "PSO (PRATICLE SWARM OPTIMIZATION)"

Styling Parameter Optimization of the Type C Recreational Vehicle Air Drag. SAE International, September 2021. http://dx.doi.org/10.4271/2021-01-5094.

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Recreational vehicles have a lot of potential consumers in China, especially the type C recreational vehicle is popular among consumers due to its advantages, prompting an increase in the production and sales volumes. The type C vehicle usually has a higher air drag than the common commercial vehicles due to its unique appearance. It can be reduced by optimizing the structural parameters, thus the energy consumed by the vehicle can be decreased. The external flow field of a recreational vehicle is analyzed by establishing its computational fluid dynamic (CFD) model. The characteristic of the RV’s external flow field is identified based on the simulation result. The approximation models of the vehicle roof parameters and air drag and vehicle volume are established by the response surface method (RSM). The vehicle roof parameters are optimized by multi-objective particle swarm optimization (MO-PSO). According to the comparison, the air drag is reduced by 2.89% and the vehicle volume is increased by 0.36%. For the RV, the proper geometry parameters can increase the inner space of the vehicle while reducing the air drag.

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