Made available in DSpace on 2014-07-29T15:08:18Z (GMT). No. of bitstreams: 1 Dissertacao Sistemas Hidrotermicos.pdf: 1921130 bytes, checksum: 988097a7877583ede959085e07eade65 (MD5) Previous issue date: 2012-06-21 / Particle Swarm Optimization has been widely used to solve real-world problems, including the operation planning of hydrothermal generation systems, where the main goal is to achieve rational strategies of operation. This can be accomplished by minimizing the high-cost thermoelectric generation, while maximizing the low-cost hydroelectric generation. The optimization process must consider a set of complex constrains. This work presents the application of some recently proposed Particle Swarm Optimizers for a group of hydroelectric power plants of the Brazilian interconnected system, using real data from existing plants. There were performed some tests by using the standard PSO, PSO-TVAC, Clan PSO, Clan PSO with migration, Center PSO, and one approach proposed in this work, called Center Clan PSO, over three different mid-term periods. All PSO approaches were compared to the results achieved by a Non-linear Programming algorithm (NLP). Furthermore, another approach was proposed, based on Center PSO, named Extended Center PSO. It was observed that the PSO approaches presented as promising solutions to the problem, even better than NLP in some cases. / A Otimização por Enxame de Partículas tem sido amplamente utilizada na solução de problemas do mundo real, inclusive para o problema do planejamento da operação de sistemas de geração hidrotérmicos, em que o principal objetivo é encontrar estratégias racionais de operação. A solução é obtida através da minimização da geração térmica, alto custo, enquanto maximiza-se a geração hidrelétrica, que é de baixo custo. O processo de otimização deve considerar um conjunto complexo de restrições. Este trabalho apresenta a aplicação de uma abordagem recente chamada de Otimização por Enxame de Partículas para o problema com um grupo de usinas hidrelétricas do sistema interligado brasileiro, utilizando dados reais das usinas existentes. Foram realizados testes usando o PSO original, PSO-TVAC, Clan PSO, Clan PSO com a migração, Center PSO, e uma abordagem proposta neste trabalho, denominada Center Clan PSO, ao longo de três diferentes períodos de médio prazo. Todas as abordagens PSO foram comparadas com os resultados obtidos por um algoritmo de programação não linear (NLP). Além disso, uma outra abordagem foi proposta, com base no algoritmo Center PSO, chamada Extended Center PSO. Observou-se que as abordagens PSO apresentaram resultados promissores na solução do problema, com resultados até mesmo melhores, em alguns casos, que os obtidos pelo NLP.

In this thesis, the effect of pitch based optimisation was explored for a 80 turbine wind farm. Using a modified Jensen wake model and the Particle Swarm Optimisation (PSO) model, a pitch optimisation strategy was created for the dominant turbulence and atmospheric condition for the wind farm. As the wake model was based on the FLORIS model developed by P.M.O Gebraad et. al., the wake and power model was compared with the FLORIS model and a -0.090% difference was found. To determine the dynamic predictive capability of the wake model, measurement values across a 10 minute period for a 19 wind turbine array were used and the wake model under predicted the power production by 17.55%. Despite its poor dynamic predictive capability, the wake model was shown to accurately match the AEP production of the wind farm when compared to a CFD simulation done in FarmFlow and only gave a 3.10% over-prediction. When the optimisation model was applied with 150 iterations and particles, the AEP production of the wind farm increased by 0.1052%, proving that the pitch optimisation method works for the examined wind farm. When the iterations and particles used for the optimisation was increased to 250, the power improvement between optimised results improved by 0.1144% at a 222.5% increase in computational time, suggesting that the solution has yet to fully converge. While the solutions did not fully converge, they converged sufficiently and an increase in iterations gave diminishing results. From the results, the pitch optimisation model was found to give a significant increase in power production, especially in wake intensive wind directions. However, the dynamic predictive capabilities will have be improved upon before the control strategy can be applied to an operational wind farm.

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

Cette thèse s’inscrit dans la thématique d’évaluation des ouvrages par des méthodes nondestructives. Le double objectif est de développer un code permettant d’effectuer au choixl’identification de paramètres par analyse inverse en utilisant un algorithme méta heuristique, ou dedéfinir une métrologie optimale (nombre de capteurs, positions, qualité) sur une structure, en vued’une identification de paramètres. Nous avons développé un code permettant de répondre à cesdeux objectifs. Il intègre des mesures in situ, un modèle mécanique aux éléments finis de lastructure étudiée et un algorithme d’optimisation méta heuristique appelé algorithme d’optimisationpar essaim particulaire. Ce code a d’abord été utilisé afin de caractériser l’influence de la métrologiesur l’identification de paramètres par analyse inverse, puis, en phase expérimentale, nous avonstravaillé sur des problèmes d’interactions sol structure. Un travail a également été réalisé surl’identification et la caractérisation de défauts par sollicitations au marteau d’impact. Enfin unexemple d’optimisation de métrologie (nombre de capteurs, positions et qualité) a été réalisé enutilisant le code original adapté pour cette étude. / This thesis deals with non-destructive evaluation in civil engineering. The objective is of two-fold:developing a code that will identify mechanical parameters by inverse analysis using a metaheuristicalgorithm, and developing another code to optimize the sensors placement (with respect tothe number and quality of the sensors) in order to identify mechanical parameters with the bestaccuracy. Our code integrates field data, a finite element model of the studying structure and aparticle swarm optimization algorithm to answer those two objectives. This code was firstly used tofocus on how the sensors placement, the number of used sensors, and their quality impact theaccuracy of parameters’ identification. Then, an application on a soil structure interaction wasconducted. Several tests to identify and characterize defaults using an impact hammer were alsocarried on. The last application focused on the optimization of the metrology in order to identifymechanical parameters with the best accuracy. This last work highlights the possibilities of theseresearches for structural health monitoring applications in civil engineering project.

Le domaine de la traction électrique a suscité un très grand intérêt dans les dernières années. La conception optimale de l'ensemble moteur électrique de traction – onduleur doit prendre en compte une variété de critères et contraintes. Étant donnée la liaison entre la géométrie du moteur et la stratégie de commande de l'onduleur, l'optimisation de l'ensemble de traction doit prendre en considération, en même temps, les deux composants.L'objectif de la thèse est la conception d'un outil d'optimisation appliqué à un système de traction électrique légère qu'emploie un moteur à réluctance variable alimenté (MRVCE) par un onduleur triphasé en pont complet. Le MRVCE est modélisé en utilisant la technique par réseau de perméances. En même temps, la technique de commande électronique peut être facilement intégrée dans le modèle pour effectuer l'analyse dynamique du fonctionnement du moteur. L'outil d'optimisation réalisé utilise l'algorithme par essaim de particules, modifié pour résoudre des problèmes multi-objectif. Les objectifs sont liés à la qualité des caractéristiques de fonctionnement du moteur, en temps que les variables d'optimisation concernent la géométrie du moteur aussi que la technique de commande. Les performances de l'algorithme sont comparées avec ceux de l'algorithme génétique (NSGA-II) et d'une implémentation classique de l'algorithme par essaim de particules multi-objectif.Finalement, un prototype de moteur à réluctance variable est construit et le fonctionnement du MRVCE alimenté depuis l'onduleur triphasé en pont complet est implémenté et les outils de modélisation et d'optimisation sont validés / The interest for the electric traction applications has been growing in the last few years. The optimal design of the electric motor and of the inverter that powers it needs to consider a long list of restrictions and criteria. Because of the fact that the geometry of the motor and the switching strategy are closely linked, the optimization of the traction solution needs to consider both, at the same time.The objective of this thesis is the development of an optimization tool applied for the optimization of an electric traction solution that uses the switched reluctance motor (SRM) fed from a three phase full bridge inverter. The SRM is modeled using Permeance Network Analysis (PNA). The switching technique can be easily integrated in the model, which gives the possibility to run a dynamic analysis. The optimization tool created uses the Particle Swarm Optimization (PSO) algorithm, modified for multi-objective problems. The algorithms performances are compared with those of the Genetic Algorithm, using the NSGA-II multi-objective technique and with a classic version of multiple objective particle swarm optimizer (MOPSO).Finally, a SRM prototype is constructed and the drive solution using a full-bridge three phase inverter is implemented. The modeling and optimization tools are thus experimentally validated

Nous nous intéressons dans cette thèse à la résolution du problème de tournées sélectives (Team Orienteering Problem - TOP) et ses variantes. Ce problème est une extension du problème de tournées de véhicules en imposan tcertaines limitations de ressources. Nous proposons un algorithme de résolution exacte basé sur la programmation linéaire en nombres entiers (PLNE) en ajoutant plusieurs inégalités valides capables d’accélérer la résolution. D’autre part, en considérant des périodes de travail strictes pour chaque véhicule durant sa tournée, nous traitons une des variantes du TOP qui est le problème de tournées sélectives multipériodique (multiperiod TOP - mTOP) pour lequel nous développons une métaheuristique basée sur l’optimisation par essaim pour le résoudre. Un découpage optimal est proposé pour extraire la solution optimale de chaque particule en considérant les tournées saturées et pseudo saturées .Finalement, afin de prendre en considération la disponibilité des clients, une fenêtre de temps est associée à chacun d’entre eux, durant laquelle ils doivent être servis. La variante qui en résulte est le problème de tournées sélectives avec fenêtres de temps (TOP with Time Windows - TOPTW). Deux algorithmes exacts sont proposés pour résoudre ce problème. Le premier est basé sur la génération de colonnes et le deuxième sur la PLNE à laquelle nous ajoutons plusieurs coupes spécifiques à ce problème. / We focus in this thesis on developing new algorithms to solve the Team Orienteering Problem (TOP) and two of its variants. This problem derives from the well-known vehicle routing problem by imposing some resource limitations .We propose an exact method based on Mixed Integer Linear Programming (MILP) to solve this problem by adding valid inequalities to speed up its solution process. Then, by considering strict working periods for each vehicle during its route, we treat one of the variants of TOP, which is the multi-period TOP (mTOP) for which we develop a metaheuristic based on the particle swarm optimization approach to solve it. An optimal split procedure is proposed to extract the optimal solution from each particle by considering saturated and pseudo-saturated routes. Finally, in order to take into consideration the availability of customers, a time window is associated with each of them, during which they must be served. The resulting variant is the TOP with Time Windows (TOPTW). Two exact algorithms are proposed to solve this problem. The first algorithm is based on column generation approach and the second one on the MILP to which we add additional cuts specific for this problem. The comparison between our exact and heuristic methods with the existing one in the literature shows the effectiveness of our approaches.

Cette thèse propose un concept d'optimisation de la consommation d'énergie dans les maisons intelligentes basées sur la gestion de la demande qui repose sur l'utilisation de système d e gestion de l'énergie à la maison (HEMS) qui est en mesure de contrôler les appareils ménagers. L'avantage de ce concept est l'optimisation de la consommation d'énergie sans réduire les utilisateurs vivant confort. Un mécanisme adaptatif pour une croissance intelligente système de gestion de l'énergie de la maison qui a composé des algorithmes qui régissent l'utilisation des différents types de charges par ordre de priorité pré-sélectionné dans la maison intelligente est proposé. En outre, une méthode pourl'optimisation de la puissance générée à partir d'un hybride de systèmes d'énergie renouvelables (HRES) afin d'obtenir la demande de charge. particules technique d'optimisation essaim (PSO) est utilisé comme l'optimisation algorithme de recherche en raison de ses avantages par rapport à d'autres techniques pour réduire le coût moyen actualisé de l'énergie (LCE) avec une plage acceptable de la production en tenant compte des pertes entre la production et la demande. Le problème est défini et la fonction objective est introduite en tenant compte des valeurs de remise en forme de sensibilité dans le processus d’essaim de particules. La structure de l'algorithme a été construite en utilisant un logiciel MATLAB et Arduino 1.0.5 du logiciel.Ce travail atteint le but de réduire la charge de l'électricité et la coupure du rapport pic-moyenne (PAR). / This thesis proposes a concept of power consumption optimization in smart homes based on demand side management that reposes on using Home Energy Management System (HEMS) that is able to control home appliances. The advantage of the concept is optimizing power consumption without reducing the users living comfort. An adaptive mechanism for smart home energy management system which composed of algorithms that govern the use of different types of loads in order of pre-selected priority in smart home is proposed. In addition a method for the optimization of the power generated from a Hybrid Renewable Energy Systems (HRES) in order to achieve the load demand. Particle Swarm Optimization Technique (PSO) is used as optimization searching algorithm due to its advantages over other techniques for reducing the Levelized Cost of Energy (LCE) with an acceptable range of the production taking into consideration the losses between production and demand sides. The problem is defined and the objective function is introduced taking into consideration fitness values sensitivity in particle swarm process. The algorithm structure was built using MATLAB software and Arduino 1.0.5 Software. This work achieves the purpose of reducing electricity expense and clipping the Peak-toAverage Ratio (PAR). The experimental setup for the smart meter implementing HEMS is built relying on the Arduino Mega 2560 board as a main controller and a web application of URL http://www.smarthome-em.com to interface with the proposed smart meter using the Arduino WIFI Shield.

Recently, continuous-valued optimization problems have received a great amount of focus, resulting in optimization algorithms which are very efficient within the continuous-valued space. Many optimization problems are, however, defined within the binary-valued problem space. These continuous-valued optimization algorithms can not operate directly on a binary-valued problem representation, without algorithm adaptations because the mathematics used within these algorithms generally fails within a binary problem space. Unfortunately, such adaptations may alter the behavior of the algorithm, potentially degrading the performance of the original continuous-valued optimization algorithm. Additionally, binary representations present complications with respect to increasing problem dimensionality, interdependencies between dimensions, and a loss of precision. This research investigates the possibility of applying continuous-valued optimization algorithms to solve binary-valued problems, without requiring algorithm adaptation. This is achieved through the application of a mapping technique, known as angle modulation. Angle modulation effectively addresses most of the problems associated with the use of a binary representation by abstracting a binary problem into a four-dimensional continuous-valued space, from which a binary solution is then obtained. The abstraction is obtained as a bit-generating function produced by a continuous-valued algorithm. A binary solution is then obtained by sampling the bit-generating function. This thesis proposes a number of population-based angle-modulated continuous-valued algorithms to solve binary-valued problems. These algorithms are then compared to binary algorithm counterparts, using a suite of benchmark functions. Empirical analysis will show that the angle-modulated continuous-valued algorithms are viable alternatives to binary optimization algorithms. Copyright 2012, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. Please cite as follows: Pamparà, G 2012, Angle modulated population based algorithms to solve binary problems, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-02242012-090312 / > C12/4/188/gm / Dissertation (MSc)--University of Pretoria, 2012. / Computer Science / unrestricted

Topology design of distributed local area networks (DLANs) can be classified as an NP-hard problem. Intelligent algorithms, such as evolutionary and swarm intelligence techniques, are candidate approaches to address this problem and to produce desirable solutions. DLAN topology design consists of several conflicting objectives such as minimization of cost, minimization of network delay, minimization of the number of hops between two nodes, and maximization of reliability. It is possible to combine these objectives in a single-objective function, provided that the trade-offs among these objectives are adhered to. This thesis proposes a strategy and a new aggregation operator based on fuzzy logic to combine the four objectives in a single-objective function. The thesis also investigates the use of a number of evolutionary algorithms such as stochastic evolution, simulated evolution, and simulated annealing. A number of hybrid variants of the above algorithms are also proposed. Furthermore, the applicability of swarm intelligence techniques such as ant colony optimization and particle swarm optimization to topology design has been investigated. All proposed techniques have been evaluated empirically with respect to their algorithm parameters. Results suggest that simulated annealing produced the best results among all proposed algorithms. In addition, the hybrid variants of simulated annealing, simulated evolution, and stochastic evolution generated better results than their respective basic algorithms. Moreover, a comparison of ant colony optimization and particle swarm optimization shows that the latter generated better results than the former. / Thesis (PhD)--University of Pretoria, 2009. / Computer Science / unrestricted

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