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Sun, Yanxia. "Improved particle swarm optimisation algorithms." Thesis, Paris Est, 2011. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000395.
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D. Tech. Electrical Engineering.Particle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called "particles" conceptualized as moving points in a multidimensional space. This thesis presents several algorithms/techniques to improve the PSO's global search ability. Simulation and analytical results confirm the efficiency of the proposed algorithms/techniques when compared to the other state of the art algorithms.
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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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Rahman, Izaz Ur. "Novel particle swarm optimization algorithms with applications in power systems." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12219.
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Optimization problems are vital in physical sciences, commercial and finance matters. In a nutshell, almost everyone is the stake-holder in certain optimization problems aiming at minimizing the cost of production and losses of system, and also maximizing the profit. In control systems, the optimal configuration problems are essential that have been solved by various newly developed methods. The literature is exhaustively explored for an appropriate optimization method to solve such kind of problems. Particle Swarm Optimization is found to be one of the best among several optimization methods by analysing the experimental results. Two novel PSO variants are introduced in this thesis. The first one is named as N State Markov Jumping Particle Swarm Optimization, which is based on the stochastic technique and Markov chain in updating the particle velocity. We have named the second variant as N State Switching Particle Swarm Optimization, which is based on the evolutionary factor information for updating the velocity. The proposed algorithms are then applied to some widely used mathematical benchmark functions. The statistical results of 30 independent trails illustrate the robustness and accuracy of the proposed algorithms for most of the benchmark functions. The better results in terms of mean minimum evaluation errors and the shortest computation time are illustrated. In order to verify the satisfactory performance and robustness of the proposed algorithms, we have further formulated some basic applications in power system operations. The first application is about the static Economic Load Dispatch and the second application is on the Dynamic Economic Load Dispatch. These are highly complex and non-linear problems of power system operations consisting of various systems and generator constraints. Basically, in the static Economic Load Dispatch, a single load is considered for calculating the cost function. In contrast, the Dynamic Economic Load Dispatch changes the load demand for the cost function dynamically with time. In such a challenging and complex environment the proposed algorithms can be applied. The empirical results obtained by applying both of the proposed methods have substantiated their adaptability and robustness into the real-world environment. It is shown in the numerical results that the proposed algorithms are robust and accurate as compared to the other algorithms. The proposed algorithms have produced consistent best values for their objectives, where satisfying all constraints with zero penalty.
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Muthuswamy, Shanthi. "Discrete particle swarm optimization algorithms for orienteering and team orienteering problems." Diss., Online access via UMI:, 2009.
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Gardner, Matthew J. "A Speculative Approach to Parallelization in Particle Swarm Optimization." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/3012.
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Particle swarm optimization (PSO) has previously been parallelized primarily by distributing the computation corresponding to particles across multiple processors. In this thesis we present a speculative approach to the parallelization of PSO that we refer to as SEPSO. In our approach, we refactor PSO such that the computation needed for iteration t+1 can be done concurrently with the computation needed for iteration t. Thus we can perform two iterations of PSO at once. Even with some amount of wasted computation, we show that this approach to parallelization in PSO often outperforms the standard parallelization of simply adding particles to the swarm. SEPSO produces results that are exactly equivalent to PSO; this is not a new algorithm or variant, only a new method of parallelization. However, given this new parallelization model we can relax the requirement of exactly reproducing PSO in an attempt to produce better results. We present several such relaxations, including keeping the best speculative position evaluated instead of the one corresponding to the standard behavior of PSO, and speculating several iterations ahead instead of just one. We show that these methods dramatically improve the performance of parallel PSO in many cases, giving speed ups of up to six times compared to previous parallelization techniques.
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Kelman, Alexander. "Utilizing Swarm Intelligence Algorithms for Pathfinding in Games." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.
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The Ant Colony Optimization and Particle Swarm Optimization are two Swarm Intelligence algorithms often utilized for optimization. Swarm Intelligence relies on agents that possess fragmented knowledge, a concept not often utilized in games. The aim of this study is to research whether there are any benefits to using these Swarm Intelligence algorithms in comparison to standard algorithms such as A* for pathfinding in a game. Games often consist of dynamic environments with mobile agents, as such all experiments were conducted with dynamic destinations. Algorithms were measured on the length of their path and the time taken to calculate that path. The algorithms were implemented with minor modifications to allow them to better function in a grid based environment. The Ant Colony Optimization was modified in regards to how pheromone was distributed in the dynamic environment to better allow the algorithm to path towards a mobile target. Whereas the Particle Swarm Optimization was given set start positions and velocity in order to increase initial search space and modifications to increase particle diversity. The results obtained from the experimentation showcased that the Swarm Intelligence algorithms were capable of performing to great results in terms of calculation speed, they were however not able to obtain the same path optimality as A*. The algorithms' implementation can be improved but show potential to be useful in games.
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Latiff, Idris Abd. "Global-adaptive particle swarm optimisation algorithms for single and multi-objective optimisation problems." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.548633.
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Zukhruf, Febri. "FREIGHT TRANSPORT NETWORK DESIGN WITH SUPPLY CHAIN NETWORK EQUILIBRIUM MODELS AND PARTICLE SWARM OPTIMISATION ALGORITHMS." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/192168.
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Szöllösi, Tomáš. "Evoluční algoritmy." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219654.
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The task of this thesis was focused on comparison selected evolutionary algorithms for their success and computing needs. The paper discussed the basic principles and concepts of evolutionary algorithms used for optimization problems. Author programmed selected evolutionary algorithms and subsequently tasted on various test functions with exactly the given input conditions. Finally the algorithms were compared and evaluated the results obtained for different settings.
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Morcos, Karim M. "Genetic network parameter estimation using single and multi-objective particle swarm optimization." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/9207.
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Master of ScienceDepartment of Electrical and Computer Engineering
Sanjoy Das
Stephen M. Welch
Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.
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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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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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Raj, Ashish. "Evolutionary Optimization Algorithms for Nonlinear Systems." DigitalCommons@USU, 2013. http://digitalcommons.usu.edu/etd/1520.
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Many real world problems in science and engineering can be treated as optimization problems with multiple objectives or criteria. The demand for fast and robust stochastic algorithms to cater to the optimization needs is very high. When the cost function for the problem is nonlinear and non-differentiable, direct search approaches are the methods of choice. Many such approaches use the greedy criterion, which is based on accepting the new parameter vector only if it reduces the value of the cost function. This could result in fast convergence, but also in misconvergence where it could lead the vectors to get trapped in local minima. Inherently, parallel search techniques have more exploratory power. These techniques discourage premature convergence and consequently, there are some candidate solution vectors which do not converge to the global minimum solution at any point of time. Rather, they constantly explore the whole search space for other possible solutions. In this thesis, we concentrate on benchmarking three popular algorithms: Real-valued Genetic Algorithm (RGA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). The DE algorithm is found to out-perform the other algorithms in fast convergence and in attaining low-cost function values. The DE algorithm is selected and used to build a model for forecasting auroral oval boundaries during a solar storm event. This is compared against an established model by Feldstein and Starkov. As an extended study, the ability of the DE is further put into test in another example of a nonlinear system study, by using it to study and design phase-locked loop circuits. In particular, the algorithm is used to obtain circuit parameters when frequency steps are applied at the input at particular instances.
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RUEDA, CAMILO VELASCO. "ESNPREDICTOR: TIME SERIES FORECASTING APPLICATION BASED ON ECHO STATE NETWORKS OPTIMIZED BY GENETICS ALGORITHMS AND PARTICLE SWARM OPTIMIZATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24785@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE EXCELENCIA ACADEMICA
A previsão de séries temporais é fundamental na tomada de decisões de curto, médio e longo prazo, em diversas áreas como o setor elétrico, a bolsa de valores, a meteorologia, entre outros. Tem-se na atualidade uma diversidade de técnicas e modelos para realizar essas previsões, mas as ferramentas estatísticas são as mais utilizadas principalmente por apresentarem um maior grau de interpretabilidade. No entanto, as técnicas de inteligência computacional têm sido cada vez mais aplicadas em previsão de séries temporais, destacando-se as Redes Neurais Artificiais (RNA) e os Sistemas de Inferência Fuzzy (SIF). Recentemente foi criado um novo tipo de RNA, denominada Echo State Networks (ESN), as quais diferem das RNA clássicas por apresentarem uma camada escondida com conexões aleatórias, denominada de Reservoir (Reservatório). Este Reservoir é ativado pelas entradas da rede e pelos seus estados anteriores, gerando o efeito de Echo State (Eco), fornecendo assim um dinamismo e um desempenho melhor para tarefas de natureza temporal. Uma dificuldade dessas redes ESN é a presença de diversos parâmetros, tais como Raio Espectral, Tamanho do Reservoir e a Percentual de Conexão, que precisam ser calibrados para que a ESN forneça bons resultados. Portanto, este trabalho tem como principal objetivo o desenvolvimento de uma ferramenta computacional capaz de realizar previsões de séries temporais, baseada nas ESN, com ajuste automático de seus parâmetros por Particle Swarm Optimization (PSO) e Algoritmos Genéticos (GA), facilitando a sua utilização pelo usuário. A ferramenta computacional desenvolvida oferece uma interface gráfica intuitiva e amigável, tanto em termos da modelagem da ESN, quanto em termos de realização de eventuais pré-processamentos na série a ser prevista.
The time series forecasting is critical to decision making in the short, medium and long term in several areas such as electrical, stock market, weather and industry. Today exist different techniques to model this forecast, but statistics are more used, because they have a bigger interpretability, due by the mathematic models created. However, intelligent techniques are being more applied in time series forecasting, where the principal models are the Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS). A new type of ANN called Echo State Networks (ESN) was created recently, which differs from the classic ANN in a randomly connected hidden layer called Reservoir. This Reservoir is activated by the network inputs, and the historic of the reservoir activations generating so, the Echo State and giving to the network more dynamism and a better performance in temporal nature tasks. One problem with these networks is the presence of some parameters as, Spectral Radius, Reservoir Size and Connection Percent, which require calibration to make the network provide positive results. Therefore the aim of this work is to develop a computational application capable to do time series forecasting, based on ESN, with automatic parameters adjustment by Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), facilitating its use by the user. The developed computational tool offers an intuitive and friendly interface, both in terms of modeling the ESN, and in terms of achievement of possible pre-process on the series to be forecasted.
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Liu, Fang. "Nature inspired computational intelligence for financial contagion modelling." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/8208.
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Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market.
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Al-Obaidi, Mohanad. "ENAMS : energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/5187.
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Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of the wireless communication and the performance of networking protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis contributes toward the design of a new hybrid optimization algorithm; ENAMS (Energy optimizatioN Algorithm for Mobile Sensor networks) which is based on the Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks. The presented algorithm is suitable for large scale mobile sensor networks and provides a robust and energy- efficient communication mechanism by dividing the sensor-nodes into clusters, where the number of clusters is not predefined and the sensors within each cluster are not necessary to be distributed in the same density. The presented algorithm enables the sensor nodes to move as swarms within the search space while keeping optimum distances between the sensors. To verify the objectives of the proposed algorithm, the LEGO-NXT MIND-STORMS robots are used to act as particles in a moving swarm keeping the optimum distances while tracking each other within the permitted distance range in the search space.
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Noudjiep, Djiepkop Giresse Franck. "Feeder reconfiguration scheme with integration of renewable energy sources using a Particle Swarm Optimisation method." Thesis, Cape Peninsula University of Technology, 2018. http://hdl.handle.net/20.500.11838/2712.
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Thesis (Master of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2018.A smart grid is an intelligent power delivery system integrating traditional and advanced control, monitoring, and protection systems for enhanced reliability, improved efficiency, and quality of supply. To achieve a smart grid, technical challenges such as voltage instability; power loss; and unscheduled power interruptions should be mitigated. Therefore, future smart grids will require intelligent solutions at transmission and distribution levels, and optimal placement & sizing of grid components for optimal steady state and dynamic operation of the power systems. At distribution levels, feeder reconfiguration and Distributed Generation (DG) can be used to improve the distribution network performance. Feeder reconfiguration consists of readjusting the topology of the primary distribution network by remote control of the tie and sectionalizing switches under normal and abnormal conditions. Its main applications include service restoration after a power outage, load balancing by relieving overloads from some feeders to adjacent feeders, and power loss minimisation for better efficiency. On the other hand, the DG placement problem entails finding the optimal location and size of the DG for integration in a distribution network to boost the network performance. This research aims to develop Particle Swarm Optimization (PSO) algorithms to solve the distribution network feeder reconfiguration and DG placement & sizing problems. Initially, the feeder reconfiguration problem is treated as a single-objective optimisation problem (real power loss minimisation) and then converted into a multi-objective optimisation problem (real power loss minimisation and load balancing). Similarly, the DG placement problem is treated as a single-objective problem (real power loss minimisation) and then converted into a multi-objective optimisation problem (real power loss minimisation, voltage deviation minimisation, Voltage stability Index maximisation). The developed PSO algorithms are implemented and tested for the 16-bus, the 33-bus, and the 69-bus IEEE distribution systems. Additionally, a parallel computing method is developed to study the operation of a distribution network with a feeder reconfiguration scheme under dynamic loading conditions.
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Allen, Andy. "Analytic element modeling of the High Plains Aquifer: non-linear model optimization using Levenberg-Marquardt and particle swarm algorithms." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/14103.
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Master of ScienceDepartment of Civil Engineering
David R. Steward
Accurate modeling of the High Plains Aquifer depends on the availability of good data that represents and quantities properties and processes occurring within the aquifer. Thanks to many previous studies there is a wealth of good data available for the High Plains Aquifer but one key component, groundwater-surface water interaction locations and rates, is generally missing. Without these values accurate modeling of the High Plains Aquifer is very difficult to achieve. This thesis presents methods for simplifying the modeling of the High Plains Aquifer using a sloping base method and then applying mathematical optimization techniques to locate and quantify points of groundwater-surface water interaction. The High Plains Aquifer has a base that slopes gently from west to east and is approximated using a one-dimensional stepping base model. The model was run under steady-state predevelopment conditions using readily available GIS data representing aquifer properties such as hydraulic conductivity, bedrock elevation, recharge, and the predevelopment water level. The Levenberg-Marquardt and particle swarm algorithms were implemented to minimize error in the model. The algorithms reduced model error by finding locations in the aquifer of potential groundwater-surface water interaction and then determining the rate of groundwater to surface water exchange at those points that allowed for the best match between the measured predevelopment water level and the simulated water level. Results from the model indicate that groundwater-surface water interaction plays an important role in the overall water balance in the High Plains Aquifer. Findings from the model show strong groundwater-surface water interaction occurring in the northern basin of the aquifer where the water table is relatively shallow and there are many surface water features. In the central and southern basins the interaction is primarily limited to river valleys. Most rivers have baseflow that is a net sink from groundwater.
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Bhandare, Ashray Sadashiv. "Bio-inspired Algorithms for Evolving the Architecture of Convolutional Neural Networks." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1513273210921513.
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Alkindy, Bassam. "Combining approaches for predicting genomic evolution." Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2012/document.
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En bio-informatique, comprendre comment les molécules d’ADN ont évolué au cours du temps reste un problème ouvert etcomplexe. Des algorithmes ont été proposés pour résoudre ce problème, mais ils se limitent soit à l’évolution d’un caractèredonné (par exemple, un nucléotide précis), ou se focalisent a contrario sur de gros génomes nucléaires (plusieurs milliardsde paires de base), ces derniers ayant connus de multiples événements de recombinaison – le problème étant NP completquand on considère l’ensemble de toutes les opérations possibles sur ces séquences, aucune solution n’existe à l’heureactuelle. Dans cette thèse, nous nous attaquons au problème de reconstruction des séquences ADN ancestrales en nousfocalisant sur des chaînes nucléotidiques de taille intermédiaire, et ayant connu assez peu de recombinaison au coursdu temps : les génomes de chloroplastes. Nous montrons qu’à cette échelle le problème de la reconstruction d’ancêtrespeut être résolu, même quand on considère l’ensemble de tous les génomes chloroplastiques complets actuellementdisponibles. Nous nous concentrons plus précisément sur l’ordre et le contenu ancestral en gènes, ainsi que sur lesproblèmes techniques que cette reconstruction soulève dans le cas des chloroplastes. Nous montrons comment obtenirune prédiction des séquences codantes d’une qualité telle qu’elle permette ladite reconstruction, puis comment obtenir unarbre phylogénétique en accord avec le plus grand nombre possible de gènes, sur lesquels nous pouvons ensuite appuyernotre remontée dans le temps – cette dernière étant en cours de finalisation. Ces méthodes, combinant l’utilisation d’outilsdéjà disponibles (dont la qualité a été évaluée) à du calcul haute performance, de l’intelligence artificielle et de la biostatistique,ont été appliquées à une collection de plus de 450 génomes chloroplastiquesIn Bioinformatics, understanding how DNA molecules have evolved over time remains an open and complex problem.Algorithms have been proposed to solve this problem, but they are limited either to the evolution of a given character (forexample, a specific nucleotide), or conversely focus on large nuclear genomes (several billion base pairs ), the latter havingknown multiple recombination events - the problem is NP complete when you consider the set of all possible operationson these sequences, no solution exists at present. In this thesis, we tackle the problem of reconstruction of ancestral DNAsequences by focusing on the nucleotide chains of intermediate size, and have experienced relatively little recombinationover time: chloroplast genomes. We show that at this level the problem of the reconstruction of ancestors can be resolved,even when you consider the set of all complete chloroplast genomes currently available. We focus specifically on the orderand ancestral gene content, as well as the technical problems this raises reconstruction in the case of chloroplasts. Weshow how to obtain a prediction of the coding sequences of a quality such as to allow said reconstruction and how toobtain a phylogenetic tree in agreement with the largest number of genes, on which we can then support our back in time- the latter being finalized. These methods, combining the use of tools already available (the quality of which has beenassessed) in high performance computing, artificial intelligence and bio-statistics were applied to a collection of more than450 chloroplast genomes
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Hnízdilová, Bohdana. "Registrace ultrazvukových sekvencí s využitím evolučních algoritmů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442502.
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This master´s thesis deals with the registration of ultrasound sequences using evolutionary algorithms. The theoretical part of the thesis describes the process of image registration and its optimalization using genetic and metaheuristic algorithms. The thesis also presents problems that may occur during the registration of ultrasonographic images and various approaches to their registration. In the practical part of the work, several optimization methods for the registration of a number of sequences were implemented and compared.
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Stacha, Radek. "Optimalizace kogeneračního systému." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231502.
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Master thesis is focused on optimization of cogeneration system for purpose of rating optimization methods and evaluating properties of these methods. For each method there is description together with block schemes. First part of thesis is devoted to description of methods and their comparison. Second part consists of development of hybrid algorithm, which is used to optimize cogeneration systém model. Each algorithm compared is together with hybrid algorithms included in annexes.
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Gadde, Srimanth. "Graph Partitioning Algorithms for Minimizing Inter-node Communication on a Distributed System." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1376561814.
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Ras, Marthinus Nicolaas. "The development of some rotationally invariant population based optimization methods." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/80172.
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Thesis (MScEng)--Stellenbosch University, 2013.ENGLISH ABSTRACT: In this study we consider the lack of rotational invariance of three different population based optimization methods, namely the particle swarm optimization (PSO) algorithm, the differential evolution (DE) algorithm and the continuous-parameter genetic algorithm (CPGA). We then propose rotationally invariant versions of these algorithms. We start with the PSO. The so-called classical PSO algorithmis known to be variant under rotation, whereas the linear PSO is rotationally invariant. This invariance however, comes at the cost of lack of diversity, which renders the linear PSO inferior to the classical PSO. The previously proposed so-called diverse rotationally invariant (DRI) PSO is an algorithm that aims to combine both diversity and invariance. This algorithm is rotationally invariant in a stochastic sense only. What is more, the formulation depends on the introduction of a random rotation matrix S, but invariance is only guaranteed for ‘small’ rotations in S. Herein, we propose a formulation which is diverse and strictly invariant under rotation, if still in a stochastic sense only. To do so, we depart with the linear PSO, and then we add a self-scaling random vector with a standard normal distribution, sampled uniformly from the surface of a n-dimensional unit sphere. For the DE algorithm, we show that the classic DE/rand/1/bin algorithm, which uses constant mutation and standard crossover, is rotationally variant. We then study a previously proposed rotationally invariant DE formulation in which the crossover operation takes place in an orthogonal base constructed using Gramm-Schmidt orthogonalization. We propose two new formulations by firstly considering a very simple rotationally invariant formulation using constant mutation and whole arithmetic crossover. This rudimentary formulation performs badly, due to lack of diversity. We then introduce diversity into the formulation using two distinctly different strategies. The first adjusts the crossover step by perturbing the direction of the linear combination between the target vector and the mutant vector. This formulation is invariant in a stochastic sense only. We add a self-scaling random vector to the unaltered whole arithmetic crossover vector. This formulation is strictly invariant, if still in a stochastic sense only. In this study we consider the lack of rotational invariance of three different population based optimization methods, namely the particle swarm optimization (PSO) algorithm, the differential evolution (DE) algorithm and the continuous-parameter genetic algorithm (CPGA). We then propose rotationally invariant versions of these algorithms. We start with the PSO. The so-called classical PSO algorithmis known to be variant under rotation, whereas the linear PSO is rotationally invariant. This invariance however, comes at the cost of lack of diversity, which renders the linear PSO inferior to the classical PSO. The previously proposed so-called diverse rotationally invariant (DRI) PSO is an algorithm that aims to combine both diversity and invariance. This algorithm is rotationally invariant in a stochastic sense only. What is more, the formulation depends on the introduction of a random rotation matrix S, but invariance is only guaranteed for ‘small’ rotations in S. Herein, we propose a formulation which is diverse and strictly invariant under rotation, if still in a stochastic sense only. To do so, we depart with the linear PSO, and then we add a self-scaling random vector with a standard normal distribution, sampled uniformly from the surface of a n-dimensional unit sphere. For the DE algorithm, we show that the classic DE/rand/1/bin algorithm, which uses constant mutation and standard crossover, is rotationally variant. We then study a previously proposed rotationally invariant DE formulation in which the crossover operation takes place in an orthogonal base constructed using Gramm-Schmidt orthogonalization. We propose two new formulations by firstly considering a very simple rotationally invariant formulation using constant mutation and whole arithmetic crossover. This rudimentary formulation performs badly, due to lack of diversity. We then introduce diversity into the formulation using two distinctly different strategies. The first adjusts the crossover step by perturbing the direction of the linear combination between the target vector and the mutant vector. This formulation is invariant in a stochastic sense only. We add a self-scaling random vector to the unaltered whole arithmetic crossover vector. This formulation is strictly invariant, if still in a stochastic sense only. For the CPGA we show that a standard CPGA using blend crossover and standard mutation, is rotationally variant. To construct a rotationally invariant CPGA it is possible to modify the crossover operation to be rotationally invariant. This however, again results in loss of diversity. We introduce diversity in two ways: firstly using a modified mutation scheme, and secondly, following the same approach as in the PSO and the DE, by adding a self-scaling random vector to the offspring vector. This formulation is strictly invariant, albeit still in a stochastic sense only. Numerical results are presented for the variant and invariant versions of the respective algorithms. The intention of this study is not the contribution of yet another competitive and/or superior population based algorithm, but rather to present formulations that are both diverse and invariant, in the hope that this will stimulate additional future contributions, since rotational invariance in general is a desirable, salient feature for an optimization algorithm.
AFRIKAANSE OPSOMMING: In hierdie studie bestudeer ons die gebrek aan rotasionele invariansie van drie verskillende populasiegebaseerde optimeringsmetodes, met name die partikel-swerm optimerings (PSO) algoritme, die differensi¨ele evolusie (DE) algoritme en die kontinue-parameter genetiese algoritme (KPGA). Ons stel dan rotasionele invariante weergawes van hierdie algoritmes voor. Ons beginmet die PSO. Die sogenaamde klassieke PSO algoritme is bekend dat dit variant is onder rotasie, terwyl die lineˆere PSO rotasioneel invariant is. Hierdie invariansie lei tot ’n gebrek aan diversiteit in die algoritme, wat beteken dat die lineˆere PSO minder goed presteer as die klassieke PSO. Die voorheen voorgestelde sogenaamde diverse rotasionele invariante (DRI) PSO is ’n algoritme wat beoog om beide diversiteit en invariansie te kombineer. Hierdie algoritme is slegs rotasioneel invariant in ’n stogastiese sin. Boonop is die formulering afhanklik van ’n willekeurige rotasie matriks S, maar invariansie is net gewaarborg vir ’klein’ rotasies in S. In hierdie studie stel ons ’n formulering voor wat divers is en streng invariant onder rotasie, selfs al is dit steeds net in ’n stogastiese sin. In hierdie formulering, vertrek ons met die lineˆere PSO, en voeg dan ’n self-skalerende ewekansige vektor met ’n standaard normaalverdeling by, wat eenvormig van die oppervlakte van ’n n-dimensionele eenheid sfeer geneem word. Vir die DE algoritme toon ons aan dat die klassieke DE/rand/1/bin algoritme, wat gebruik maak van konstante mutasie en standaard kruising rotasioneel variant is. Ons bestudeer dan ’n voorheen voorgestelde rotasionele invarianteDE formulering waarin die kruisingsoperasie plaasvind in ’n ortogonale basis wat gekonstrueer wordmet behulp van die Gramm-Schmidt ortogonalieseringsproses. Verder stel ons dan twee nuwe formulerings voor deur eerstens ’n baie eenvoudige rotasionele invariante formulering te oorweeg, wat konstante mutasie en volledige rekenkundige kruising gebruik. Hierdie elementˆere formulering onderpresteer as gevolg van die afwesigheid van diversiteit. Ons voeg dan diversiteit by die formulering toe, deur gebruik te maak van twee afsonderlike strategie ¨e. Die eerste verander die kruisings stap deur die rigting van die lineˆere kombinasie tussen die teiken vektor en die mutasie vektor te perturbeer. Hierdie formulering is slegs invariant in ’n stogastiese sin. In die ander formulering, soos met die nuwe rotasionele invariante PSO, voeg ons bloot ’n self-skalerende ewekansige vektor by die onveranderde volledige rekenkundige kruisingsvektor. Hierdie formulering is streng invariant onder rotasie, selfs al is dit steeds net in ’n stogastiese sin. Vir die KPGA wys ons dat die standaard KPGA wat gemengde kruising en standaard mutasies gebruik, rotasioneel variant is. Om ’n rotasionele invariante KPGA te konstrueer is dit moontlik om die kruisingsoperasie aan te pas. Dit veroorsaak weereens ’n verlies aan diversiteit. Ons maak die algoritmes divers op twee verskillende maniere: eerstens deur gebruik te maak van ’n gewysigde mutasie skema, en tweedens deur die selfde aanslag te gebruik as in die PSO en die DE, deur ’n self-skalerende ewekansige vektor by die nageslag vektor te voeg. Hierdie formulering is streng invariant onder rotasie, selfs al is dit steeds net in ’n stogastiese sin. Numeriese resultate word vir die variante en invariante weergawe van die onderskeie algoritmes verskaf. Die doel van hierdie studie is nie die bydrae van bloot nog ’n kompeterend en/of beter populasiegebaseerde optimeringsmetode nie, maar eerder om formulerings voor te lê wat beide divers en invariant is, met die hoop dat dit in die toekoms bykomende bydraes sal stimuleer, omdat rotasionele invariansie in die algemeen ’n aantreklike, belangrike kenmerk is vir ’n optimerings algoritme.
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Abdussalam, Fathi M. A. "Antenna design using optimization techniques over various computaional electromagnetics. Antenna design structures using genetic algorithm, Particle Swarm and Firefly algorithms optimization methods applied on several electromagnetics numerical solutions and applications including antenna measurements and comparisons." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17217.
Full textAbstract:
Dealing with the electromagnetic issue might bring a sort of discontinuous and nondifferentiable
regions. Thus, it is of great interest to implement an appropriate optimisation
approach, which can preserve the computational resources and come up with a global
optimum. While not being trapped in local optima, as well as the feasibility to overcome some
other matters such as nonlinear and phenomena of discontinuous with a large number of
variables.
Problems such as lengthy computation time, constraints put forward for antenna
requirements and demand for large computer memory, are very common in the analysis due
to the increased interests in tackling high-scale, more complex and higher-dimensional
problems. On the other side, demands for even more accurate results always expand
constantly. In the context of this statement, it is very important to find out how the recently
developed optimization roles can contribute to the solution of the aforementioned problems.
Thereafter, the key goals of this work are to model, study and design low profile antennas for
wireless and mobile communications applications using optimization process over a
computational electromagnetics numerical solution. The numerical solution method could be
performed over one or hybrid methods subjective to the design antenna requirements and
its environment.
Firstly, the thesis presents the design and modelling concept of small uni-planer Ultra-
Wideband antenna. The fitness functions and the geometrical antenna elements required for
such design are considered. Two antennas are designed, implemented and measured. The
computed and measured outcomes are found in reasonable agreement. Secondly, the work
is also addressed on how the resonance modes of microstrip patches could be performed
using the method of Moments. Results have been shown on how the modes could be
adjusted using MoM. Finally, the design implications of balanced structure for mobile
handsets covering LTE standards 698-748 MHz and 2500-2690 MHz are explored through
using firefly algorithm method. The optimised balanced antenna exhibits reasonable
matching performance including near-omnidirectional radiations over the dual desirable
operating bands with reduced EMF, which leads to a great immunity improvement towards
the hand-held.General Secretariat of Education and Scientific Research Libya
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Abusitta, M. M. "Design and modelling of beam steering antenna array for mobile and wireless applications using optimisation algorithms. Simulation and measrement of switch and phase shifter for beam steering antenna array by applying reactive loading and time modulated switching techniques, optimised using genetic algorithms and particle swarm methods." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5745.
Full textAbstract:
The objectives of this work were to investigate, design and implement beam steering antenna arrays for
mobile and wireless applications using the genetic algorithm (GA) and particle swarm optimisation (PSO)
techniques as optimisation design tools. Several antenna designs were implemented and tested: initially, a
printed dipole antenna integrated with a duplex RF switch used for mobile base station antenna beam
steering was investigated. A coplanar waveguide (CPW) to coplanar strip (CPS) transition was adopted to
feed the printed dipole. A novel RF switch circuit, used to control the RF signal fed to the dipole antenna
and placed directly before it, was proposed. The measured performance of the RF switch was tested and
the results confirmed its viability. Then two hybrid coupled PIN diode phase shifters, using Branchline
and Rat-Race ring coupler structures, were designed and tested. The generation of four distinct phase
shifts was implemented and studied. The variations of the scattering parameters were found to be realistic,
with an acceptable ±2 phase shift tolerance.
Next, antenna beam steering was achieved by implementing RF switches with ON or OFF mode
functions to excite the radiating elements of the antenna array. The switching control process was
implemented using a genetic algorithm (GA) method, subject to scalar and binary genes. Anti-phase
feeding of radiating elements was also investigated. A ring antenna array with reflectors was modelled
and analysed. An antenna of this type for mobile base stations was designed and simulation results are
presented.
Following this, a novel concept for simple beam steering using a uniform antenna array operated at 2.4
GHz was designed using GA. The antenna is fed by a single RF input source and the steering elements
are reactively tuned by varactor diodes in series with small inductors. The beam-control procedure was
derived through the use of a genetic algorithm based on adjusting the required reactance values to obtain
the optimum solution as indicated by the cost function. The GA was also initially used as an optimisation
tool to derive the antenna design from its specification.
Finally, reactive loading and time modulated switching techniques are applied to steer the beam of a
circular uniformly spaced antenna array having a source element at its centre. Genetic algorithm (GA)
and particle swarm optimisation (PSO) processes calculate the optimal values of reactances loading the
parasitic elements, for which the gain can be optimised in a desired direction. For time modulated
switching, GA and PSO also determine the optimal on and off times of the parasitic elements for which
the difference in currents induced optimises the gain and steering of the beam in a desired direction.
These methods were demonstrated by investigating a vertically polarised antenna configuration. A
prototype antenna was constructed and experimental results compared with the simulations. Results
showed that near optimal solutions for gain optimisation, sidelobe level reduction and beam steering are
achievable by utilising these methods. In addition, a simple switching process is employed to steer the
beam of a horizontally polarised circular antenna array. A time modulated switching process is applied
through Genetic Algorithm optimisation. Several model examples illustrate the radiation beams and the
switching time process of each element in the array.
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Abusitta, Musa M. "Design and modelling of beam steering antenna array for mobile and wireless applications using optimisation algorithms : simulation and measrement of switch and phase shifter for beam steering antenna array by applying reactive loading and time modulated switching techniques, optimised using genetic algorithms and particle swarm methods." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5745.
Full textAbstract:
The objectives of this work were to investigate, design and implement beam steering antenna arrays for mobile and wireless applications using the genetic algorithm (GA) and particle swarm optimisation (PSO) techniques as optimisation design tools. Several antenna designs were implemented and tested: initially, a printed dipole antenna integrated with a duplex RF switch used for mobile base station antenna beam steering was investigated. A coplanar waveguide (CPW) to coplanar strip (CPS) transition was adopted to feed the printed dipole. A novel RF switch circuit, used to control the RF signal fed to the dipole antenna and placed directly before it, was proposed. The measured performance of the RF switch was tested and the results confirmed its viability. Then two hybrid coupled PIN diode phase shifters, using Branchline and Rat-Race ring coupler structures, were designed and tested. The generation of four distinct phase shifts was implemented and studied. The variations of the scattering parameters were found to be realistic, with an acceptable ±2 phase shift tolerance. Next, antenna beam steering was achieved by implementing RF switches with ON or OFF mode functions to excite the radiating elements of the antenna array. The switching control process was implemented using a genetic algorithm (GA) method, subject to scalar and binary genes. Anti-phase feeding of radiating elements was also investigated. A ring antenna array with reflectors was modelled and analysed. An antenna of this type for mobile base stations was designed and simulation results are presented. Following this, a novel concept for simple beam steering using a uniform antenna array operated at 2.4 GHz was designed using GA. The antenna is fed by a single RF input source and the steering elements are reactively tuned by varactor diodes in series with small inductors. The beam-control procedure was derived through the use of a genetic algorithm based on adjusting the required reactance values to obtain the optimum solution as indicated by the cost function. The GA was also initially used as an optimisation tool to derive the antenna design from its specification. Finally, reactive loading and time modulated switching techniques are applied to steer the beam of a circular uniformly spaced antenna array having a source element at its centre. Genetic algorithm (GA) and particle swarm optimisation (PSO) processes calculate the optimal values of reactances loading the parasitic elements, for which the gain can be optimised in a desired direction. For time modulated switching, GA and PSO also determine the optimal on and off times of the parasitic elements for which the difference in currents induced optimises the gain and steering of the beam in a desired direction. These methods were demonstrated by investigating a vertically polarised antenna configuration. A prototype antenna was constructed and experimental results compared with the simulations. Results showed that near optimal solutions for gain optimisation, sidelobe level reduction and beam steering are achievable by utilising these methods. In addition, a simple switching process is employed to steer the beam of a horizontally polarised circular antenna array. A time modulated switching process is applied through Genetic Algorithm optimisation. Several model examples illustrate the radiation beams and the switching time process of each element in the array.
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Li, Zeyuan. "Target localization using RSS measurements in wireless sensor networks." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31356.
Full textAbstract:
The subject of this thesis is the development of localization algorithms for target localization in wireless sensor networks using received signal strength (RSS) measurements or Quantized RSS (QRSS) measurements. In chapter 3 of the thesis, target localization using RSS measurements is investigated. Many existing works on RSS localization assumes that the shadowing components are uncorrelated. However, here, shadowing is assumed to be spatially correlated. It can be shown that localization accuracy can be improved with the consideration of correlation between pairs of RSS measurements. By linearizing the corresponding Maximum Likelihood (ML) objective function, a weighted least squares (WLS) algorithm is formulated to obtain the target location. An iterative technique based on Newtons method is utilized to give a solution. Numerical simulations show that the proposed algorithms achieves better performance than existing algorithms with reasonable complexity. In chapter 4, target localization with an unknown path loss model parameter is investigated. Most published work estimates location and these parameters jointly using iterative methods with a good initialization of path loss exponent (PLE). To avoid finding an initialization, a global optimization algorithm, particle swarm optimization (PSO) is employed to optimize the ML objective function. By combining PSO with a consensus algorithm, the centralized estimation problem is extended to a distributed version so that can be implemented in distributed WSN. Although suboptimal, the distributed approach is very suitable for implementation in real sensor networks, as it is scalable, robust against changing of network topology and requires only local communication. Numerical simulations show that the accuracy of centralized PSO can attain the Cramer Rao Lower Bound (CRLB). Also, as expected, there is some degradation in performance of the distributed PSO with respect to the centralized PSO. In chapter 5, a distributed gradient algorithm for RSS based target localization using only quantized data is proposed. The ML of the Quantized RSS is derived and PSO is used to provide an initial estimate for the gradient algorithm. A practical quantization threshold designer is presented for RSS data. To derive a distributed algorithm using only the quantized signal, the local estimate at each node is also quantized. The RSS measurements and the local estimate at each sensor node are quantized in different ways. By using a quantization elimination scheme, a quantized distributed gradient method is proposed. In the distributed algorithm, the quantization noise in the local estimate is gradually eliminated with each iteration. Simulations show that the performance of the centralized algorithm can reach the CRLB. The proposed distributed algorithm using a small number of bits can achieve the performance of the distributed gradient algorithm using unquantized data.
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Deus, Guilherme Resende. "Otimização de sistemas hidrotérmicos de geração por meio de meta-heurísticas baseadas em enxame de partículas." Universidade Federal de Goiás, 2016. http://repositorio.bc.ufg.br/tede/handle/tede/7530.
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The objective of this work is to find reasonable solutions to the problem of optimization of hydrothermal generating systems by means of metaheuristics based on particle swarms. The proposed problem is complex, dynamic, nonlinear and presents some stochastic variables. The study consisted of the implementation of particle swarm algorithms, more specifically the variants of the Particle Swarm Optimization (PSO) algorithm: LSSPSO, ABeePSO and KFPSO. The algorithms were run in a mill simulator containing data from eight National Interconnected System mills during the five year period. The results were compared with the studies using the Nonlinear Programming (NLP) algorithm, and it was concluded that although the presented meta-heuristics were able to obtain a Final Storage Energy value equal to NLP, they did not have a generation cost Equivalent to or less than the Nonlinear Programming method.
O trabalho objetiva encontrar soluções razoáveis para o problema de otimização de sistemas hidrotérmicos de geração por meio de meta-heurísiticas baseadas em enxame de partículas. O problema proposto é complexo, dinâmico, não linear e apresenta algumas variáveis estocásticas. O estudo consistiu na implementação de algoritmos baseados em enxame de partículas, mais especificamente das variantes do algoritmo Particle Swarm Optimization (PSO): LSSPSO, ABeePSO e KFPSO. Os algoritmos foram executados em um simulador de usinas que contém dados de oito usinas do Sistema Interligado Nacional durante o período de cinco anos. Os resultados foram comparados com os estudos que utilizam o algoritmo de Programação Não-Linear (PNL), e conclui-se que apesar de as meta-heurísticas apresentadas conseguirem obter um valor de Energia Armazenada Final igual ao PNL, não obtiveram um custo de geração equivalente ou inferior ao método de Programação Não-Linear.
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Haji, Agha Mohammad Zarbaf Seyed Ehsan. "Vibration-based Cable Tension Estimation in Cable-Stayed Bridges." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535636861655531.
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RIBEIRO, Reiga Ramalho. "Reconstrução de imagens de tomografia por impedância elétrica usando evolução diferencial." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/17899.
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CAPES
A Tomografia por Impedância Elétrica (TIE) é uma técnica que visa reconstruir imagens do interior de um corpo de forma não-invasiva e não-destrutiva. Com base na aplicação de corrente elétrica e na medição dos potenciais de borda do corpo, feita através de eletrodos, um algoritmo de reconstrução de imagens de TIE gera o mapa de condutividade elétrica do interior deste corpo. Diversos métodos são aplicados para gerar imagens de TIE, porém ainda são geradas imagens de contorno suave. Isto acontece devido à natureza matemática do problema de reconstrução da TIE como um problema mal-posto e mal-condicionado. Isto significa que não existe uma distribuição de condutividade interna exata para uma determinada distribuição de potenciais de borda. A TIE é governada matematicamente pela equação de Poisson e a geração da imagem envolve a resolução iterativa de um problema direto, que trata da obtenção dos potenciais de borda a partir de uma distribuição interna de condutividade. O problema direto, neste trabalho, foi aplicado através do Método dos Elementos Finitos. Desta forma, é possível aplicar técnicas de busca e otimização que objetivam minimizar o erro médio quadrático relativo (função objetivo) entre os potenciais de borda mensurados no corpo (imagem ouro) e os potencias gerados pela resolução do problema direto de um candidato à solução. Assim, o objetivo deste trabalho foi construir uma ferramenta computacional baseada em algoritmos de busca e otimização híbridos, com destaque para a Evolução Diferencial, a fim de reconstruir imagens de TIE. Para efeitos de comparação também foram utilizados para gerar imagens de TIE: Algoritmos Genéticos, Otimização por Enxame de Partículas e Recozimento Simulado. As simulações foram feitas no EIDORS, uma ferramenta usada em MatLab/ GNU Octave com código aberto voltada para a comunidade de TIE. Os experimentos foram feitos utilizando três diferentes configurações de imagens ouro (fantomas). As análises foram feitas de duas formas, sendo elas, qualitativa: na forma de o quão as imagens geradas pela técnica de otimização são parecidas com seu respectivo fantoma; quantitativa: tempo computacional, através da evolução do erro relativo calculado pela função objetivo do melhor candidato à solução ao longo do tempo de reconstrução das imagens de TIE; e custo computacional, através da avaliação da evolução do erro relativo ao longo da quantidade de cálculos da função objetivo pelo algoritmo. Foram gerados resultados para Algoritmos Genéticos, cinco versões clássicas de Evolução Diferencial, versão modificada de Evolução Diferencial, Otimização por Enxame de Partículas, Recozimento Simulado e três novas técnicas híbridas baseadas em Evolução Diferencial propostas neste trabalho. De acordo com os resultados obtidos, vemos que todas as técnicas híbridas foram eficientes para resolução do problema da TIE, obtendo bons resultados qualitativos e quantitativos desde 50 iterações destes algoritmos. Porém, merece destacar o rendimento do algoritmo obtido pela hibridização da Evolução Diferencial e Recozimento Simulado por ser a técnica aqui proposta mais promissora na reconstrução de imagens de TIE, onde mostrou ser mais rápida e menos custosa computacionalmente do que as outras técnicas propostas. Os resultados desta pesquisa geraram diversas contribuições na forma de artigos publicados em eventos nacionais e internacionais.
Electrical Impedance Tomography (EIT) is a technique that aim to reconstruct images of the interior of a body in a non-invasive and non-destructive form. Based on the application of the electrical current and on the measurement of the body’s edge electrical potential, made through of electrodes, an EIT image reconstruction algorithm generates the conductivity distribution map of this body’s interior. Several methods are applied to generate EIT images; however, they are still generated smooth contour images. This is due of the mathematical nature of EIT reconstruction problem as an ill-posed and ill-conditioned problem. Thus, there is not an exact internal conductivity distribution for one determinate edge potential distribution. The EIT is ruled mathematically by Poisson’s equations, and the image generation involves an iterative resolution of a direct problem, that treats the obtainment of the edge potentials through of an internal distribution of conductivity. The direct problem, in this dissertation, was applied through of Finite Elements Method. Thereby, is possible to apply search and optimization techniques that aim to minimize the mean square error relative (objective function) between the edge potentials measured in the body (gold image) and the potential generated by the resolution of the direct problem of a solution candidate. Thus, the goal of this work was to construct a computational tool based in hybrid search and optimization algorithms, highlighting the Differential Evolution, in order to reconstruct EIT images. For comparison, it was also used to generate EIT images: Genetic Algorithm, Particle Optimization Swarm and Simulated Annealing. The simulations were made in EIDORS, a tool used in MatLab/GNU Octave open source toward the TIE community. The experiments were performed using three different configurations of gold images (phantoms). The analyzes were done in two ways, as follows, qualitative: in the form of how the images generated by the optimization technique are similar to their respective phantom; quantitative: computational time, by the evolution of the relative error calculated for the objective function of the best candidate to the solution over time the EIT images reconstruction; and computational cost, by evaluating the evolution of the relative error over the amount of calculations of the objective functions by the algorithm. Results were generated for Genetic Algorithms, five classical versions of Differential Evolution, modified version of the Differential Evolution, Particle Optimization Swarm, Simulated Annealing and three new hybrid techniques based in Differential Evolution proposed in this work. According to the results obtained, we see that all hybrid techniques were efficient in solving the EIT problem, getting good qualitative and quantitative results from 50 iterations of these algorithms. Nevertheless, it deserves highlight the algorithm performance obtained by hybridization of Differential Evolution and Simulated Annealing to be the most promising technique here proposed to reconstruct EIT images, which proved to be faster and less expensive computationally than other proposed techniques. The results of this research generate several contributions in the form of published paper in national and international events.
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Srnec, Pavel. "Optimalizace nastavení závodního vozu simulátoru TORCS." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236557.
Full textAbstract:
This master's thesis is about nature optimalization technigues. Evolution algortihms together with main thesis topic, Particle Swarm Optimization, is introduced in the following chapter. Car setup and simulator TORCS are introduced in next chapter. Design and implementation are introduced in next chapters. Destination of t master's thesis is finding optimal car setups for different curcuits.
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Wilke, Daniel N. "Analysis of the particle swarm optimization algorithm." Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-01312006-125743.
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Kesler, René. "Optimalizace metaheuristikami v Pythonu pomocí knihovny DEAP." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-401489.
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{This thesis deals with optimization by means of metaheuristics, which are used for complicated engineering problems that cannot be solved by classical methods of mathematical programming. At the beginning, choosed metaheuristics are described: simulated annealing, particle swarm optimization and genetic algorithm; and then they are compared with use of test functions. These algorithms are implemented in Python programming language with use of package called DEAP, which is also described in this thesis. Algorithms are then applied for optimization of design parameters of the heat storage unit.
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Grandi, Raffaele <1976>. "Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5904/.
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This thesis presents some different techniques designed to drive a swarm of robots in an a-priori unknown environment in order to move the group from a starting area to a final one avoiding obstacles. The presented techniques are based on two different theories used alone or in combination: Swarm Intelligence (SI) and Graph Theory. Both theories are based on the study of interactions between different entities (also called agents or units) in Multi- Agent Systems (MAS). The first one belongs to the Artificial Intelligence context and the second one to the Distributed Systems context. These theories, each one from its own point of view, exploit the emergent behaviour that comes from the interactive work of the entities, in order to achieve a common goal. The features of flexibility and adaptability of the swarm have been exploited with the aim to overcome and to minimize difficulties and problems that can affect one or more units of the group, having minimal impact to the whole group and to the common main target. Another aim of this work is to show the importance of the information shared between the units of the group, such as the communication topology, because it helps to maintain the environmental information, detected by each single agent, updated among the swarm. Swarm Intelligence has been applied to the presented technique, through the Particle Swarm Optimization algorithm (PSO), taking advantage of its features as a navigation system. The Graph Theory has been applied by
exploiting Consensus and the application of the agreement protocol with the aim to maintain the units in a desired and controlled formation. This approach has been followed in order to conserve the power of PSO and to control part of its random behaviour with a distributed control algorithm
like Consensus.
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Hemzalová, Zuzana. "Evoluční algoritmy pro ultrazvukovou perfúzní analýzu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442504.
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This thesis deals with the principles of ultrasonic perfusion analysis and methods for determining perfusion parameters. It examines Evolutionary algorithms and their ability to optimize the approximation of dilution curves from ultrasond tissue scannig. It compares the optimization performance of three evolutionary algorithms. Continuous genetic algorithm GA, algorithm SOMA and PSO. Methods are evaluated on simulated and clinical data.
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Chen, Kun-Huang, and 陳昆皇. "Particle Swarm Optimization Algorithms for Feature Selection." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/75079311564407541435.
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博士國立清華大學
工業工程與工程管理學系
100
Searching for an optimal feature subset from a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient when solving large-scale feature selection problems. Therefore, meta-heuristic algorithms are extensively adopted to solve such problems efficiently. This study proposes two approaches: opposite sign test and regression-based particle swarm optimization for feature selection problem. The proposed algorithms can increase population diversity and avoid local optimal trapping by improving the jump ability of flying particles. The data sets collected from UCI machine learning data bases are used to evaluate the effectiveness of the proposed approaches. Classification accuracy is used as a criterion to evaluate classifier performance. Results show that our proposed approaches outperform both genetic algorithms and sequential search algorithms. In addition, a real case about the diagnosis of obstructive sleep apnea (OSA) using the proposed approach is presented. Through the implementation of this real case study, we found that the proposed approach could be applied as a screening tool for early OSA diagnosis. As a result, PSO can be applied to assist doctors in foreseeing the diagnosis of OSA before running the PSG test, allowing the medical resources to be used more effectively.
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Chiang, Cheng-Wen, and 江正文. "Improving Particle Swarm Optimization Algorithms with Activation Strategies." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/37386756889929024177.
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碩士中原大學
資訊管理研究所
95
According to researches, activation was a minor strategy of improving Particle Swarm Algorithm. For proofing the possibility of activation strategy could be a main strategy for improving Particle Swarm Algorithm, this article adopt the concept of mutation in Genetic Algorithm to improve Particle Swarm Optimization. The experimental results show three advantages for the activation strategies. First, It’s workable for the algorithm and the performance are superior to former PSO algorithms. Second, the agitation strategy was more flexible. Third, when the activation strategies collocate with other PSO algorithms, the performance would be superior then the original algorithm.
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Liu, Kung Liang, and 劉恭良. "Particle swarm optimization algorithms for fundamental matrix estimation." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/42180499853745256382.
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碩士國立政治大學
資訊科學學系
99
Fundamental matrix is a very important parameter in image processing. In corresponding point determination, coordinate system conversion, as well as three-dimensional model reconstruction, etc., fundamental matrix always plays an important role. Hence, obtaining an accurate fundamental matrix becomes one of the most important issues in image processing. In this paper, we present a mechanism that uses the concept of Particle Swarm Optimization (PSO) to find fundamental matrix. Our approach not only can improve the accuracy of the fundamental matrix but also can reduce computation costs. After using Scale-Invariant Feature Transform (SIFT) to get a large number of corresponding points from the multi-view images, we choose a set of eight corresponding points, based on the image resolutions, grouping principles, together with random sampling, as our initial starting points for PSO. Least Median of Squares (LMedS) is used in estimating the initial fitness value as well as the minimal number of iterations in PSO. The fundamental matrix can then be computed using the PSO algorithm. We use different objects to illustrate our mechanism and compare the results obtained by using PSO and using LMedS. The experimental results show that, if we use the same number of iterations in the experiments, the fundamental matrix computed by the PSO method have better estimated average error than that computed by the LMedS method. Also, the PSO method takes about one-eighth of the time required for the LMedS method in these computations.
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Huang, Z. L., and 黃子倫. "Particle Swarm Optimization Algorithms Based On the Behavior Analyses." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/54876858152420247148.
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碩士國立宜蘭大學
電機工程學系碩士班
97
In this thesis, a new behavior analyzed multimodal particle swarm optimization (BAMPSO) algorithm is proposed for not only unimodal problems but multi-modal problems. The main idea is to find the local minima by analyzing the variation of the fitness value when the particles are moving. Since almost all the local minima are found, the global minimum can be obviously obtained. That is, the BAMPSO can avoid converging to local solution and efficiently find the global solution. Moreover, the behavior analyzed adaptive particle swarm optimization (BAAPSO) algorithm based on the same idea to on-line search the global minimum is also provided. BAAPSO algorithm can on-line to adjust parameters and improve the accuracy on searching for multi-objection problem. Experiment results and comparisons with other PSO algorithms are included to indicate the effectiveness of the proposed BAMPSO and BAAPSO algorithms.
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Wu, Chih-Jyz, and 吳昌志. "Modified Particle Swarm Optimization Algorithms for the Traveling Salesman Problem." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/20600614853314927681.
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碩士中原大學
資訊管理研究所
95
According Particle Swarm Optimiztion Algorithm, is an swarm intelligence Algorithm. Throught simulate birds behavior,each particle share information each other to reach the best result. But the Particle Swarm Optimiztion Algorithm is good at the continue function,be bad at discrete function. For proofing then Particle Swarm Optimiztion Algorithm can be used at discrete problem, this article combine Particle Swarm Optimiztion Algorithm with 2-Opt algorithm and Greed mtheod to slove Traveling salesman problem. The performance would be superior then the original algorithm.
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楊禮瑛. "Particle swarm optimization algorithms for the open vehicle routing problem." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/74853641796586654630.
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Chen, Wan-Ren, and 陳萬仁. "Applications of Particle Swarm Optimization Algorithms for Wireless Sensor Networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/e9u8c2.
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碩士國立臺中科技大學
資訊工程系碩士班
100
The main features of wireless sensors are energy limited, hence, the algorithm design with the enhancement of energy efficiency becomes an important topic. In this thesis, we propose two energy efficiency algorithms. One is PSO-LEACH algorithm with Taguchi algorithm which associates LEACH and PSO to select the optimal cluster heads from a wireless cluster sensor network. In the WSNs, PSO algorithm is used to select cluster head and reach the energy dissipation balance among all nodes, which can reduce the probability of the early death of the nodes. In PSO algorithm, the selection of parameter is very important, hence, in order to achieve optimal parameter value, too much number of experiments is usually spent, therefore, in this thesis, Taguchi method was adopted to reduce the number of experiments and the energy dissipation of the node. Consequently, we can get the optimal parameter value of PSO algorithm, and the energy dissipation of each node can then be balanced. From the simulation result, it can be seen that our proposed PSO-LEACH architecture with Taguchi algorithm has reduced energy dissipation as compared to LEACH architecture and PSO-LEACH architecture. Our second energy efficiency algorithm is called Hierarchical Clustering of Energy Efficiency with Particle Swarm Optimization (HCEE-PSO) algorithm for three-layer cluster architecture, divided into cluster and sub-cluster phases. We propose the Total Routing Cost ( ) to balance the energy of the sensor node, the sub-cluster head and the cluster head. In the sub-cluster head selection phase, we use the PSO algorithm to search for the suitable sub-cluster head. The cost function of the PSO algorithm ensures that the distance between the sub-cluster head and cluster head is not too close and that the amount of sub-cluster head energy is close to the cluster head energy. The entire WSNs is divided into three phases of transmission to reduce the energy consumption of the cluster members and disperse the energy consumption of the cluster head. Simulation results show that our proposed HCEE-PSO algorithm reduces the energy consumption of WSNs and so extends their lifetime more than do LEACH architecture, the EECS algorithm or the MOECS algorithm.
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Wu, Jane-Jang, and 吳建漳. "Solving N-Queen Problem Using Repository Particle Swarm Optimization Algorithms." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/13722748794647121269.
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碩士元智大學
資訊管理學系
95
This research studies a storage grain of PSO algorithm method (Reposition PSO) to be called (RPSO), through RPSO the heuristic method and the storage empirical value memory way, makes PSO to solve in the question on has the efficiency, this article then processes each particle individual penetration dimension transformation matrix way the N-Queen Problem, through this method construction model to deal with the N-Queen Problem issue, simultaneously stores up the granule by the array method the discrete state and provides the calculating method to feed into the use, the reasonable application in the N-Queen Problem, this article by RPSO with other type of Algorithm method and the Gas Algorithm method (Genetic Algorithms, Ga) will confirm RPSO the potency to surpass other algorithm method, and confirmed the RPSO algorithm to compare has the validity and the uniformity.
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Khoerniawan, Airlangga, and 艾蘭格. "Truck Routing for Perishable Products by Particle Swarm Optimization Based Algorithms." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/85301761277321898334.
Full textAbstract:
碩士國立交通大學
運輸與物流管理學系
103
The level of consumption milk in Indonesia is increased every year so as the level of production of milk is increasing. In 2010, Indonesia is the 2nd largest milk production country in ASEAN. In Indonesia, farmers belong to the cooperatives called GKSIs, which are responsible for helping farmers to store and sell milk. One of the largest GKSIs is KPBS Pangalengan, located in Bandung. KPBS Pangalengan cooperates with milk product producers to establish the 1st Tier Milk Treatment (MT 1) and the External Cooling Units for processing fresh milk. At the moment, there are five cooling units scattered in five different locations. Meanwhile, there are 33 registered milk collection points (called TPKs) associated with the active member KPBS PANGALENGAN. Because milk is a product highly perishable, the delivery of fresh milk should be sent to the cooling of milk within a specific time limit. The issue is in addition to the issue of the milk collection time windows. On the other hand, the trucks have different capacities, and the cooling facilities also have a limited capacity. The trucks leave the MT 1 to begin a tour and must end the tour at an ending point with a cooling facility. Thus, the problem is classified as open vehicle routing problem with time window and heterogeneous fleet mix (OVRP-TWHF). In order to solve the problem, an IP Model was developed to get the global optimal solution where the objective is to minimize total distance. Although the optimal solution can be derived by the model, the time for the IP solve to converge can be very long. Because of that, two meta-heuristic algorithms were developed to reduce the computational time and generate an acceptable solution. Particle Swarm Optimization (PSO) and a hybrid PSO with Genetic Algorithm (GA) technique were involved. Both of them have been popular for solving the NP hard problem like OVRP. Based on the numerical experiments, the IP model is capable of solving the routing problems with about ten customers due to the computation load. The PSO and the Hybrid PSO-GA can generate the solution better than the initial solution from the classic nearest neighbor method. However, the gap between the metaheuristic method and the IP model with optimal solution is still big. However, the computational time advantage for the meta-heuristic algorithms is significant.
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Bezuidenhout, William. "Optimising the frequency assignment problem utilizing particle swarm optimisation." Thesis, 2014. http://hdl.handle.net/10210/12342.
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M.Sc. (Information Technology)A new particle swarm optimisation (PSO) algorithm that produces solutions to the xed spectrum frequency assignment problem (FS-FAP) is presented. Solutions to the FS-FAP are used to allocate frequencies in a mobile telecommunications network and must have low interference. The standard PSO algorithm's velocity method and global selection is ill suited for the frequency assignment problem (FAP). Therefore using the standard PSO algorithm as base, new techniques are developed to allow it to operate on the FAP. The new techniques include two velocity methods and three global selection schemes. This study presents the results of the algorithm operating on the Siemens set of COST 259 problems and shows that it is viable applying the PSO to the FAP.
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Liao, Kuo-Lung, and 廖國隆. "Hybrid Simulated Annealing and Particle Swarm Optimization Algorithms for Traveling Salesman Problems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/10377006719037304935.
Full textAbstract:
碩士國立金門技術學院
電資研究所
97
Traveling Salesman Problem (TSP) like the typical discrete scheduling, assigning system can be considered as an NP-Complete search problem. The heuristic Particle Swarm Optimization (PSO) algorithm is first introduced by Kennedy and Eberhart in 1995. Based on the global search capability of PSO, it has been proven to well solve continuous optimal problems. In this literature, the applied novel PSO-TS-SA algorithm with PSO, Transfer Space (TS) and Simulated Annealing (SA) schemes is integrated to find near optimal solutions for several type cities TSP problems. The Transfer Space (TS) operation in such sorting way is proposed to recover the continuous results. Simulated Annealing (SA) aimed at avoiding the trapped in local optimal condition is applied to improve the jump ability of the flying particle. Moreover, a popular Fuzzy C-means Clustering (FCM) algorithm is used to divide large TSP cities into suitable traveling groups. A novel merging algorithm is acted as a connection machine to connect the head and end of the selected cities groups. Its objective is to recovery the near optimal traveling path in a shorter spending time. Several TSP testing data set is used to demonstrate the adaptation of the proposed PSO-TS-SA in some experiment results, the PSO-TS-SA algorithm compared with other learning algorithm is illustrated its better performance. In other experiments, the popular FCM algorithm the proposed FCM clustering and merging based learning algorithm can fast approximate the desired output in large traveling salesman problem.
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Chang, Wen-Yu, and 張文郁. "Design of Optimal Grey PID and Fuzzy Controllers Using Particle Swarm Algorithms." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/uq65s2.
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碩士國立臺北科技大學
自動化科技研究所
99
This paper proposes a design method using particle swarm optimization (PSO) and Nelder-Mead PSO (NMPSO) to adjust the parameters of PID controllers and fuzzy controllers so that we can avoid that in traditional to design of the controller parameters often requires expert experience or training samples. We propose the grey model based on the grey system theory to combine with PID control to establish the PID prediction control system so that we can improve the instability which cause by noise in the real system. It both needs the expertise to design of the fuzzy rules of fuzzy controls and the parameters of PID controllers; therefore we use the algorithm to design the fuzzy rules so that we don''t have to waste a lot of time to design the rules. In simulation, we use simple fuzzy controllers which have the parameter search by the algorithm. By design the better fuzzy controllers which can control the inverted pendulum system keeping balance and stable.
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(5931074), Md Saiful Islam. "Dynamic Electronic Asset Allocation Comparing Genetic Algorithm with Particle Swarm Optimization." Thesis, 2019.
Find full textAbstract:
The contribution of this research work can be divided into two main tasks: 1) implementing this Electronic Warfare Asset Allocation Problem (EWAAP) with the Genetic Algorithm (GA); 2) Comparing performance of Genetic Algorithm to Particle Swarm Optimization (PSO) algorithm. This research problem implemented Genetic Algorithm in C++ and used QT Data Visualization for displaying three-dimensional space, pheromone, and Terrain. The Genetic algorithm implementation maintained and preserved the coding style, data structure, and visualization from the PSO implementation. Although the Genetic Algorithm has higher fitness values and better global solutions for 3 or more receivers, it increases the running time. The Genetic Algorithm is around (15-30%) more accurate for asset counts from 3 to 6 but requires (26-82%) more computational time. When the allocation problem complexity increases by adding 3D space, pheromones and complex terrains, the accuracy of GA is 3.71% better but the speed of GA is 121% slower than PSO. In summary, the Genetic Algorithm gives a better global solution in some cases but the computational time is higher for the Genetic Algorithm with than Particle Swarm Optimization.
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Gninkeu, Tchapda Ghislain Yanick. "Application of improved particle swarm optimization in economic dispatch of power systems." Diss., 2018. http://hdl.handle.net/10500/24428.
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Economic dispatch is an important optimization challenge in power systems. It helps to find the optimal output power of a number of generating units that satisfy the system load demand at the cheapest cost, considering equality and inequality constraints. Many nature inspired algorithms have been broadly applied to tackle it such as particle swarm optimization. In this dissertation, two improved particle swarm optimization techniques are proposed to solve economic dispatch problems. The first is a hybrid technique with Bat algorithm. Particle swarm optimization as the main optimizer integrates bat algorithm in order to boost its velocity and to adjust the improved solution. The second proposed approach is based on Cuckoo operations. Cuckoo search algorithm is a robust and powerful technique to solve optimization problems. The study investigates the effect of levy flight and random search operation in Cuckoo search in order to ameliorate the performance of the particle swarm optimization algorithm. The two improved particle swarm algorithms are firstly tested on a range of 10 standard benchmark functions and then applied to five different cases of economic dispatch problems comprising 6, 13, 15, 40 and 140 generating units.Electrical and Mining Engineering
M. Tech. (Electrical Engineering)