Pattern recognition has as its objective to classify objects into different categories and classes. It is a fundamental component of artificial intelligence and computer vision. This thesis investigates the application of an efficient optimization method, known as Particle Swarm Optimization (PSO), to the field of pattern recognition and image processing. First a clustering method that is based on PSO is proposed. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. A new automatic image generation tool tailored specifically for the verification and comparison of various unsupervised image classification algorithms is then developed. A dynamic clustering algorithm which automatically determines the "optimum" number of clusters and simultaneously clusters the data set with minimal user interference is then developed. Finally, PSO-based approaches are proposed to tackle the color image quantization and spectral unmixing problems. In all the proposed approaches, the influence of PSO parameters on the performance of the proposed algorithms is evaluated. / Thesis (PhD)--University of Pretoria, 2006. / Computer Science / unrestricted

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

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

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

Le travail présenté dans ce mémoire porte sur l'étude, l'optimisation et la réalisation d'une transmission planétaire magnétique. Dans notre thèse nous essayons de répondre à quelques questions intéressantes sur la possibilité de remplacer un train planétaire mécanique par un train planétaire magnétique, est-ce que la formule de Willis reste valable pour le train planétaire magnétique et est-ce que les trains magnétiques ont des performances similaires à celles des trains mécaniques ? Donc nous étudions, le remplacement du train mécanique par une transmission magnétique. Nous montrons que le train magnétique a un volume moindre, des pertes inférieures et plusieurs autres avantages. Notre but dans cette thèse est d'obtenir un "design" optimal d'un train magnétique. Nous utilisons un logiciel de calcul par éléments finis pour l'étude électromagnétique et nous cherchons également à optimiser les dimensions de ce train. Pour cela nous utilisons la méthode d'optimisation par essaim de particules (OEP). Un prototype a été réalisé ce qui permet de confronter les résultats de simulation et expérimentaux. / The work presented in this thesis deals with the study, the optimisation and the realisation of a magnetic planetary transmission. We try to answer some questions about the possibility of replacing the mechanical planetary gear used in industrial machines by a magnetic planetary gear; is the formula of Willis still valid for the magnetic planetary gear and are the magnetic planetary gear performances at least similar to ones of the mechanical gears? We study the replacement of the mechanical planetary gear by a magnetic one. We show that the magnetic one has a smaller volume, lower losses and many other benefits. The objective of this work is to obtain an optimum design of a magnetic planetary gear. We use a finite element software to study the magnetic behaviour of the device and we also perform the optimization of the dimensions of the magnetic planetary gear. The particle swarm optimization method (PSO) has been used. A prototype has been built so the computation results has been compared to the experimental ones.

Artificial intelligence tools, which are fast, robust and adaptive can overcome the drawbacks of traditional solutions for several power systems problems. In this work, applications of AI techniques have been studied for solving two important problems in power systems. The first problem is static security evaluation (SSE). The objective of SSE is to identify the contingencies in planning and operations of power systems. Numerical conventional solutions are time-consuming, computationally expensive, and are not suitable for online applications. SSE may be considered as a binary-classification, multi-classification or regression problem. In this work, multi-support vector machine is combined with several evolutionary computation algorithms, including particle swarm optimization (PSO), differential evolution, Ant colony optimization for the continuous domain, and harmony search techniques to solve the SSE. Moreover, support vector regression is combined with modified PSO with a proposed modification on the inertia weight in order to solve the SSE. Also, the correct accuracy of classification, the speed of training, and the final cost of using power equipment heavily depend on the selected input features. In this dissertation, multi-object PSO has been used to solve this problem. Furthermore, a multi-classifier voting scheme is proposed to get the final test output. The classifiers participating in the voting scheme include multi-SVM with different types of kernels and random forests with an adaptive number of trees. In short, the development and performance of different machine learning tools combined with evolutionary computation techniques have been studied to solve the online SSE. The performance of the proposed techniques is tested on several benchmark systems, namely the IEEE 9-bus, 14-bus, 39-bus, 57-bus, 118-bus, and 300-bus power systems. The second problem is the non-convex, nonlinear, and non-differentiable economic dispatch (ED) problem. The purpose of solving the ED is to improve the cost-effectiveness of power generation. To solve ED with multi-fuel options, prohibited operating zones, valve point effect, and transmission line losses, genetic algorithm (GA) variant-based methods, such as breeder GA, fast navigating GA, twin removal GA, kite GA, and United GA are used. The IEEE systems with 6-units, 10-units, and 15-units are used to study the efficiency of the algorithms.

Submitted by Maike Costa (maiksebas@gmail.com) on 2017-06-29T14:15:20Z No. of bitstreams: 1 arquivototal.pdf: 1397036 bytes, checksum: 303111e916d8c9feca61ed32762bf54c (MD5) / Made available in DSpace on 2017-06-29T14:15:20Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 1397036 bytes, checksum: 303111e916d8c9feca61ed32762bf54c (MD5) Previous issue date: 2017-01-31 / The Maximu m Diversity Problem (MDP) is a problem of combinatorial optimization area that aims to select a pre-set number of elements in a given set so that a sum of the differences between the selected elements are greater as possible. MDP belongs to the class of NP-Hard problems, that is, there is no known algorithm that solves in polynomial time accurately. Because they have a complexity of exponential order, require efficient heuristics to provide satisfactory results in acceptable time. However, heuristics do not guarantee the optimality of the solution found. This paper proposes a new hybrid approach for a resolution of the Maximum Diversity Problem and is based on the Particle Swarm Optimization (PSO) and Tabu Search (TS) metaheuristics, The algorithm is called PSO_TS. The use of PSO_TS achieves the best results for known instances testing in the literature, thus demonstrating be competitive with the best algorithms in terms of quality of the solutions. / O Problema da Diversidade Máxima (MDP) é um problema da área de Otimização Combinatória que tem por objetivo selecionar um número pré-estabelecido de elementos de um dado conjunto de maneira tal que a soma das diversidades entre os elementos selecionados seja a maior possível. O MDP pertence a classe de problemas NP-difícil, isto é, não existe algoritmo conhecido que o resolva de forma exata em tempo polinomial. Por apresentarem uma complexidade de ordem exponencial, exigem heurísticas eficientes que forneçam resultados satisfatórios em tempos aceitáveis. Entretanto, as heurísticas não garantem otimalidade da solução encontrada. Este trabalho propõe uma nova abordagem híbrida para a resolução do Problema da Diversidade Máxima e está baseada nas meta-heurísticas de Otimização por Nuvem de Partículas (PSO) e Busca Tabu(TS). O algoritmo foi denominado PSO_TS. Para a validação do método, os resultados encontrados são comparados com os melhores existentes na literatura.

Made available in DSpace on 2016-08-17T14:53:19Z (GMT). No. of bitstreams: 1 dissertacao Raimundo.pdf: 3146531 bytes, checksum: 40d7a999c6dda565c6701f7cc4a171aa (MD5) Previous issue date: 2011-07-28 / The defense mechanisms expansion for cyber-attacks combat led to the malware evolution, which have become more structured to break these new safety barriers. Among the numerous malware, Botnet has become the biggest cyber threat due to its ability of controlling, the potentiality of making distributed attacks and because of the existing structure of control. The intrusion detection and prevention has had an increasingly important role in network computer security. In an intrusion detection system, information about the current situation and knowledge about the attacks contribute to the effectiveness of security process against this new cyber threat. The proposed solution presents an Intrusion Detection System (IDS) model which aims to expand Botnet detectors through active objects system by proposing a technology with collect by sensors, preprocessing filter and detection based on signature and anomaly, supported by the artificial intelligence method Particle Swarm Optimization (PSO) and Artificial Neural Networks. / A ampliação dos mecanismos de defesas no uso do combate de ataques ocasionou a evolução dos malwares, que se tornaram cada vez mais estruturados para o rompimento destas novas barreiras de segurança. Dentre os inúmeros malwares, a Botnet tornou-se uma grande ameaça cibernética, pela capacidade de controle e da potencialidade de ataques distribuídos e da estrutura de controle existente. A detecção e a prevenção de intrusão desempenham um papel cada vez mais importante na segurança de redes de computadores. Em um sistema de detecção de intrusão, as informações sobre a situação atual e os conhecimentos sobre os ataques tornam mais eficazes o processo de segurança diante desta nova ameaça cibernética. A solução proposta apresenta um modelo de Sistema de Detecção de Intrusos (IDS) que visa na ampliação de detectores de Botnet através da utilização de sistemas objetos ativos, propondo uma tecnologia de coleta por sensores, filtro de pré-processamento e detecção baseada em assinatura e anomalia, auxiliado pelo método de inteligência artificial Otimização de Enxame da Partícula (PSO) e Redes Neurais Artificiais.

The maintenance schedule of generators in power plants needs to match the electricity demand and needs to ensure the reliability of the power plant at a minimum cost of operation. In this study, a comparison is made between the modified generator maintenance scheduling model and the classic generator maintenance scheduling model using the reliability objective functions. Both models are applied to a 21-unit test system, and the results show that the modified generator maintenance scheduling model gives better and more reliable solutions than the regular generator maintenance scheduling model. The better results of the modified generator maintenance scheduling model are due the modified and additional constraints in the modified generator maintenance scheduling model. Due to the reliable results of the modified generator maintenance scheduling model, a robust model is formulated using the economic cost objective function. The model includes modified crew and maintenance window constraints, with some additional constraints such as the relationship constraints among the variables. To illustrate the robustness of the formulated GMS model, the maintenance of the Arnot power plant in South Africa is scheduled with open-loop and closed-loop controllers. Both controllers satisfy all the constraints but the closed-loop results are better than the open-loop results. AFRIKAANS : Die onderhoudskedule vir kragopwekkers (OSK) in kragstasies moet kan voorsien in die vraag na elektrisiteit en moet die betroubaarheid van die kragstasie teen ’n minimum operasiekoste verseker. In hierdie studie word die betroubaarheidsdoelwitfunksie gebruik om ’n gewysigde onderhoudskeduleringsmodel vir kragopwekkers te vergelyk met die konvensionele onderhoudskeduleringsmodel. Beide modelle word toegepas op 'n 21-eenheid-toetsstelsel, en die resultate toon dat die gewysigde model ’n beter en meer betroubare oplossing bied as die konvensionele model. Die beter resultate van die gewysigde model is die gevolg van die gewysigde en bykomende beperkings in die gewysigde model. As gevolg van die betroubare resultate van die gewysigde onderhoudskeduleringsmodel word die koste-ekonomie-doelwitfunksie gebruik om ’n robuuste model te formuleer. Die model sluit gewysigde bemanning- en onderhoudvensterbeperkings in, met ’n paar bykomende beperkings soos die verhoudingsbeperkings tussen die veranderlikes. Om die robuustheid van die geformuleerde OSK-model te illustreer word die instandhouding van die Arnot kragstasie in Suid-Afrika geskeduleer met oop- en geslotelus-beheerders. Beide beheerders voldoen aan al die beperkinge, maar die geslotelusresultate is beter as die ooplusresultate. / Dissertation (MSc)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / Unrestricted

The genetic algorithm (GA) is a popular random search and optimization method inspired by the concepts of crossover, random mutation, and natural selection from evolutionary biology. The real-valued genetic algorithm (RGA) is an improved version of the genetic algorithm designed for direct operation on real-valued variables. In this work, a modified version of a genetic algorithm is introduced, which is called a modified genetic algorithm with micro-movement (MGAM). It implements a particle swarm optimization(PSO)-inspired micro-movement phase that helps to improve the convergence rate, while employing the e'cient GA mechanism for maintaining population diversity. In order to test the capability of the MGAM, we firrst implement it on five generally used test functions. Then we test the MGAM on two typical nonlinear dynamical systems. The performance of the MGAM is compared to a basic RGA on all these applications. Finally, we implement the MGAM on the most important application, which is the plasma physics-based model of the solar wind-driven magnetosphere-ionosphere system (WINDMI). In order to use this model for real-time prediction of geomagnetic activity, the model parameters require up-dating every 6-8 hours. We use the MGAM to train the parameters of the model in order to achieve the lowest mean square error (MSE) against the measured auroral electrojet (AL) and Dst indices. The performance of the MGAM is compared to the RGA on historical geomagnetic storm datasets. While the MGAM performs substantially better than the RGA when evaluating standard test functions, the improvement is about 6-12 percent when used on the 20D nonlinear dynamical WINDMI model.