This study focuses on two types of routing problems based on standard Traveling Salesman Problem, which are TSP with pickup and delivery (TSPPD) and TSP with backhauls (TSPB). In both of these problems, there are two types of customers, i.e. &ldquo / delivery customers&rdquo / demanding goods from depot and &ldquo / pickup customers&rdquo / sending goods to depot. The objective is to minimize the cost of the tour that visits every customer once without violating the side constraints. In TSPB, delivery customers should precede the pickup customers, whereas the vehicle capacity should not be exceeded in TSPPD. The aim of the study is to propose good Evolutionary Algorithms (EA) for these two problems and also analyze the adaptability of an EA, originally designed for the standard TSP, to the problems with side constraints. This effort includes commenting on the importance of feasibility of the solutions in the population with respect to these side constraints. Having this in mind, different EA strategies involving feasible or infeasible solutions are designed. These strategies are compared by quantitative experiments realized over a set of problem instances and the results are given.

Στην εργασία αυτή, αναφερθήκαμε στους εξελικτικούς αλγορίθμους, στον διαφορο-εξελικτικό αλγόριθμο ενώ μελετήσαμε πιο αναλυτικά τον γενετικό αλγόριθμο (θεωρητική και μαθηματική μελέτη). Στην συνέχεια, αναλύθηκαν τα τεχνητά νευρωνικά δίκτυα, η δομή τους, το θεωρητικό τους υπόβαθρο και έγινε μια μαθηματική προσέγγισή τους. Το επόμενο αντικείμενο αυτής της εργασίας ήταν η μελέτη και ανάλυση των ασαφών γνωστικών απεικονίσεων (θεωρητικά, μαθηματικά, χρησιμότητά τους σε διάφορα προβλήματα). Στα επόμενα κεφάλαια γίνεται αναφορά σε συγκεκριμένα παραδείγματα εκπαίδευσης και βελτιστοποίησης του μοντέλου των ασαφών γνωστικών απεικονίσεων και των τεχνητών νευρωνικών δικτύων χρησιμοποιώντας τον γενετικό αλγόριθμο και εξελικτικές έννοιες. / In this work, we discussed the evolutionary algorithms, the differentiation evolutionary algorithm and studied in more detail the genetic algorithm (theoretical and mathematical study). Then analyzed the artificial neural networks, their structure, their theoretical background and became a mathematical approach. The next object of this work was the study and analysis of fuzzy cognitive representations (in theory, mathematics, useful in different problems). The following chapters refer to specific examples of training and optimization of fuzzy model of cognitive imaging and artificial neural networks using genetic algorithm and evolutionary concepts.

A demanda criada pelos estudos biológicos resultou para predição da estrutura terciária de proteínas ser uma alternativa, uma vez que menos de 1% das sequências conhecidas possuem sua estrutura terciária determinada experimentalmente. As predições Ab initio foca nas funções baseadas da física, a qual se trata apenas das informações providas pela sequência primária. Por consequência, um espaço de busca com muitos mínimos locais ótimos deve ser pesquisado. Este cenário complexo evidencia uma carência de algoritmos eficientes para este espaço, tornando-se assim o principal obstáculo para este tipo de predição. A optimização Multi-Objetiva, principalmente os Algoritmos Evolutivos, vem sendo aplicados na predição da estrutura terciária já que na mesma se envolve um compromisso entre os objetivos. Este trabalho apresenta o framework ProtPred-PEO-GROMACS, ou simplesmente 3PG, que não somente faz predições com a mesma acurácia encontrada na literatura, mas também, permite investigar a predição por meio da manipulação de combinações de objetivos, tanto no aspecto energético quanto no estrutural. Além disso, o 3PG facilita a implementação de novas opções, métodos de análises e também novos algoritmos evolutivos. A fim de salientar a capacidade do 3PG, foi então discorrida uma comparação entre os algoritmos NSGA-II e SPEA2 aplicados na predição Ab initio da estrutura terciária de proteínas em seis combinações de objetivos. Ademais, o uso da técnica de refinamento por Dinâmica Molecular é avaliado. Os resultados foram adequados quando comparado com outras técnicas de predições: Algoritmos Evolutivo Multi-Objetivo, Replica Exchange Molecular Dynamics, PEP-FOLD e Folding@Home. / The demand created by biological studies resulted the structure prediction as an alternative, since less than 1% of the known protein primary sequences have their 3D structure experimentally determined. Ab initio predictions focus on physics-based functions, which regard only information about the primary sequence. As a consequence, a search space with several local optima must be sampled, leading to insucient sampling of this space, which is the main hindrance towards better predictions. Multi-Objective Optimization approaches, particularly the Evolutionary Algorithms, have been applied in protein structure prediction as it involves a compromise among conicting objectives. In this paper we present the ProtPred-PEO-GROMACS framework, or 3PG, which can not only make protein structure predictions with the same accuracy standards as those found in the literature, but also allows the study of protein structures by handling several energetic and structural objective combinations. Moreover, the 3PG framework facilitates the fast implementation of new objective options, method analysis and even new evolutionary algorithms. In this study, we perform a comparison between the NSGA-II and SPEA2 algorithms applied on six dierent combinations of objectives to the protein structure. Besides, the use of Molecular Dynamics simulations as a renement technique is assessed. The results were suitable when comparated with other prediction methodologies, such as: Multi-Objective Evolutionary Algorithms, Replica Exchange Molecular Dynamics, PEP-FOLD and Folding@Home.

Muitos problemas de otimização multi-objetivo utilizam os algoritmos evolutivos para encontrar as melhores soluções. Muitos desses algoritmos empregam as fronteiras de Pareto como estratégia para obter tais soluções. Entretando, conforme relatado na literatura, há a limitação da fronteira para problemas com até três objetivos, podendo tornar seu emprego insatisfatório para os problemas com quatro ou mais objetivos. Além disso, as propostas apresentadas muitas vezes eliminam o emprego dos algoritmos evolutivos, os quais utilizam tais fronteiras. Entretanto, as características dos algoritmos evolutivos os qualificam para ser empregados em problemas de otimização, como já vem sendo difundido pela literatura, evitando eliminá-lo por causa da limitação das fronteiras de Pareto. Assim sendo, neste trabalho se buscou eliminar as fronteiras de Pareto e para isso utilizou a lógica Fuzzy, mantendo-se assim o emprego dos algoritmos evolutivos. O problema escolhido para investigar essa substituição foi o problema de predição de estrutura terciária de proteínas, pois além de se encontrar em aberto é de suma relevância para a área de bioinformática. / Several multi-objective optimization problems utilize evolutionary algorithms to find the best solution. Some of these algoritms make use of the Pareto front as a strategy to find these solutions. However, according to the literature, the Pareto front limitation for problems with up to three objectives can make its employment unsatisfactory in problems with four or more objectives. Moreover, many authors, in most cases, propose to remove the evolutionay algorithms because of Pareto front limitation. Nevertheless, characteristics of evolutionay algorithms qualify them to be employed in optimization problems, as it has being spread out by literature, preventing to eliminate it because the Pareto front elimination. Thus being, this work investigated to remove the Pareto front and for this utilized the Fuzzy logic, remaining itself thus the employ of evolutionary algorithms. The choice problem to investigate this remove was the protein tertiary structure prediction, because it is a open problem and extremely relevance to bioinformatic area.

Orientador: Fernando José Von Zuben / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-18T04:29:56Z (GMT). No. of bitstreams: 1 Coelho_GuilhermePalermo_D.pdf: 7483685 bytes, checksum: 911cf6805528b86c8b1fc0f176f47e58 (MD5) Previous issue date: 2011 / Resumo: Meta-heurísticas baseadas no paradigma de Sistemas Imunológicos Artificiais (SIAs), especialmente aquelas inspiradas na teoria da Rede Imunológica, são reconhecidamente capazes de estimular a geração de conjuntos diversos de soluções para um dado problema, mesmo utilizando-se de mecanismos muito simples de controle da dinâmica da rede. Por outro lado, na literatura de SIAs há uma série de estudos que propõem modelos computacionais mais elaborados, geralmente baseados no conceito de concentração de anticorpos, que conseguem explicar melhor o comportamento dessas redes. Diante disso, neste trabalho é proposto um novo algoritmo imunoinspirado para otimização em espaços contínuos, denominado cob-aiNet (Concentration-based Artificial Immune Network), que emprega o conceito de concentração de anticorpos para promover um melhor controle da dinâmica da rede, permitindo assim obter uma melhor cobertura das regiões promissoras do espaço de busca. Esta propriedade da cob-aiNet foi verificada em uma série de análises experimentais, nas quais o algoritmo foi comparado a outras técnicas baseadas em paradigmas distintos, além de dois outros SIAs já propostos na literatura. Os experimentos mostraram que o algoritmo cob-aiNet, além de sua capacidade de manutenção de diversidade ao longo de toda a execução, é competitivo na aproximação do ótimo global dos problemas. Diante disso, também foi proposta neste trabalho uma extensão da cob-aiNet para tratar problemas de otimização multiobjetivo, denominada cob-aiNet[MO] (Concentration-based Artificial Immune Network for Multiobjective Optimization). Assim como um conjunto bem reduzido de propostas da literatura, a cob-aiNet[MO] é capaz de tratar problemas de otimização multiobjetivo que requerem uma manutenção adequada de diversidade também no espaço das variáveis de decisão, não apenas para superar as dificuldades introduzidas pela multimodalidade mas também para facilitar o processo de escolha a posteriori da solução que será efetivamente adotada na prática. Uma série de análises experimentais foram feitas com o algoritmo cob-aiNet[MO], sendo observado que esta ferramenta apresentou resultados superiores na maioria dos problemas, tanto em aproximação da fronteira de Pareto quanto em manutenção de diversidade / Abstract: Metaheuristics based on the Artificial Immune System (AIS) framework, especially those inspired by the Immune Network theory, are known to be capable of stimulating the generation of diverse sets of solutions for a given problem, even though they generally implement very simple mechanisms to control the dynamics of the network. However, there are several studies in the AIS literature that propose more elaborate computational models, generally based on the concept of concentration of antibodies, which better explain the behavior of such networks. Therefore, in this work we propose a novel immune-inspired algorithm for real-parameter optimization, named cob-aiNet (Concentrationbased Artificial Immune Network), that adopts the concept of concentration of antibodies to better control the dynamics of the network, so that a broader coverage of promising regions of the search space can be achieved. This property of cob-aiNet was verified in a series of experimental analyses, in which the algorithm was compared to several techniques based on distinct paradigms, including two popular AISs from the literature. The experiments have shown that cob-aiNet, besides being able to maintain diversity during all the iterations, is also competitive with respect to the approximation of the global optima of the problems. Therefore, it was also proposed in this work an extension of cob-aiNet to deal with multiobjective optimization problems, which was named cob-aiNet[MO] (Concentration-based Artificial Immune Network for Multiobjective Optimization). Like a small set of techniques from the literature, cob-aiNet[MO] is capable of dealing with multiobjective optimization problems that also require a proper maintenance of diversity in the decision space, not only to overcome difficulties introduced by multimodality but also to facilitate the post-optimization decision making process. A series of experimental analyses were also made with cob-aiNet[MO], and it was observed that this algorithm presented better results in most of the considered problems, with respect to both the approximation of the Pareto front and diversity maintenance / Doutorado / Engenharia de Computação / Doutor em Engenharia Elétrica

Orientadores: Raul Vinhas Ribeiro, Antonio Carlos Moretti / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-15T22:13:26Z (GMT). No. of bitstreams: 1 Quinzani_CeciliaMorais_M.pdf: 890601 bytes, checksum: 304bbc7988e7df635b107fc3346436b3 (MD5) Previous issue date: 2010 / Resumo: O desenvolvimento das áreas tradicionais da engenharia tem sido caracterizado pelo crescente emprego de modelos de otimização como paradigmas para problemas de tomada de decisão. Quando estes modelos possuem mais de um objetivo são chamados de Problemas de Otimização Multiobjetivo (POM) e uma alternativa apropriada na resolução deste tipo de problema é a utilização de Algoritmos Evolutivos. Os Algoritmos Evolutivos (AE) simulam o processo de evolução natural. Simplificadamente, o conjunto de soluções candidatas (população) sobre o qual operam as metodologias é modificado utilizando dois princípios básicos de evolução: seleção e variação. O objetivo principal desta dissertação consiste na análise da aplicação de Algoritmos Evolutivos na otimização multiobjetivo de portfólios onde o importante é obter uma correlação ótima entre retorno e risco. Diversos algoritmos evolutivos foram analisados na dissertação, sendo também analisadas versões híbridas dos mesmos. A principal contribuição da dissertação é a proposta de um procedimento de refinamento das soluções que se baseia no comportamento da série histórica para gerar uma população inicial mais adequada. Uma comparação do desempenho dos diferentes algoritmos híbridos com e sem este refinamento da solução foi realizada e o algoritmo com melhor desempenho foi identificado / Abstract: The development of traditional areas of engineering has been characterized by the increasing use of optimization models as paradigms for decision making problems. when these models have more than one objective, they are called multi-objective optimiation problems (POMs), and are a suitable alternative in solving this kind of problem is the usage of Evolutionary Algorithms (EAs). The EAs simulate the process of natural evolution. Briefly, the set of candidate solutions (population) in which the methodologies operate is modified using two basic principles of evolution: selection and variation. The main objective of this dissertation is to review the application of Evolutionary Algorithms in Multiobjective optimization of portfolios in which it is important to obtain an optimal correlation between return and risk . Several evolutionary algorithms have been analyzed in the dissertation, and also analyzed hybrid versions of the same. The main contribution of the dissertation is to propose a procedure for the refinement of solutions based on the behavior of the series to generate a better initial population. A comparison of the performance of different algorithms hybrids with and without this refinement of the solution was performed and the algorithm with best performance was identified / Mestrado / Automação / Mestre em Engenharia Elétrica

Made available in DSpace on 2016-03-15T19:37:43Z (GMT). No. of bitstreams: 1 Venyton Nathan Leandro Izidoro.pdf: 1903798 bytes, checksum: 9381a055579503451c2cb1fd8267f60f (MD5) Previous issue date: 2012-08-01 / The educational game Calangos is based on the ecological modeling of a real case of lizards that inhabit the Dunes of the Middle São Francisco River, in the state of Bahia - Brazil. The goal of the game is to enable students to interact with an environment that promotes a proper understanding of ecological and evolutionary processes. The game should serve as a tool to support the teaching and learning of ecology and evolution to high school students. For the Calangos Game to achieve this goal, central concepts of evolution and ecology should be properly incorporated in the game. Thus, the first scientific challenge of the project, in addition to the technological challenge of developing the game itself, is related to modeling the population dynamics and evolutionary biology in the context of Calangos. To investigate these aspects independently of the game, this dissertation proposes a simulator for Calangos, as well as a genetic-evolutionary model for the lizards. Furthermore, it performs a set of experiments that examine the ecology and evolution of the lizards in the proposed simulated environment. More specifically, four experimental scenarios and three difficulty levels for each environmental scenario are proposed to carefully analyze the dynamics of populations and the influence of the evolution on the fertility and longevity of lizards populations located within the simulated environment. The results clearly show that in a balanced environment without predators it is possible to observe a dynamic equilibrium of populations in a typical Lotka-Volterra model of population dynamics. On the other hand, it is also observed that in the most hostile environments containing large numbers of predators, the capability of evolution allows the lizard species to survive in the environment, which does not occur if the lizards evolution is disabled during the simulations. In the context of the Calangos game, the results presented here serve as the initial proof of concept necessary for the modeling of the lizards to be incorporated in the game. / O jogo eletrônico educativo Calangos é baseado na modelagem de um caso ecológico real relativo aos lagartos que habitam a região das Dunas do Médio São Francisco, no Estado da Bahia. O objetivo final do jogo é possibilitar ao estudante interagir com um ambiente que promova uma compreensão adequada de processos ecológicos e evolutivos da natureza. O jogo deve funcionar como ferramenta de apoio ao ensino e aprendizagem de ecologia e evolução no nível médio de escolaridade. Para que o Calangos atinja esse objetivo conceitos centrais de evolução e ecologia deverão ser incorporados adequadamente ao jogo. Nesse sentido, o primeiro desafio científico do projeto, que antecede aos desafios tecnológicos de desenvolvimento de jogos propriamente ditos, está relacionado à como modelar a dinâmica das populações e a biologia evolutiva no contexto do Calangos. Para investigar estes aspectos de forma independente do jogo, essa dissertação propõe um simulador para o Calangos, assim como uma modelagem genético-evolutiva para os lagartos e, na sequência, realiza um conjunto de experimentos que permitem analisar a ecologia e evolução dos lagartos no ambiente simulado. Mais especificamente, são propostos quatro cenários experimentais e três níveis de dificuldade ambiental para cada cenário, que permitirão analisar cuidadosamente a dinâmica das populações e influência da evolução na fecundidade e longevidade de populações de lagartos localizadas dentro do ambiente de simulação. Os resultados mostram que em um ambiente equilibrado e sem predadores é possível observar um equilíbrio dinâmico das populações, em um formato típico dos modelos clássicos de dinâmica populacional baseados nas equações de Lotka-Volterra. Por outro lado, observa-se também que em ambientes mais hostis contendo grande quantidade de predadores a capacidade de evolução dos lagartos permite a sobrevivência da espécie no ambiente, o que não ocorre caso os lagartos não possam evoluir durante as simulações. No contexto do jogo Calangos, os resultados apresentados aqui servem como a prova de conceito inicial necessária para a modelagem computacional dos lagartos a serem incorporados no jogo.

Service Oriented Computing (SOC) provides a framework for the realization of loosely couple service oriented applications (SOA). Web services are central to the concept of SOC. They possess several benefits which are useful to SOA e.g. encapsulation, loose coupling and reusability. Using web services, an application can embed its functionalities within the business process of other applications. This is made possible through web service composition. Web services are composed to provide more complex functions for a service consumer in the form of a value added composite service. Currently, research into how web services can be composed to yield QoS (Quality of Service) optimal composite service has gathered significant attention. However, the number and services has risen thereby increasing the number of possible service combinations and also amplifying the impact of network on composite service performance. QoS-based service composition in the cloud addresses two important sub-problems; Prediction of network performance between web service nodes in the cloud, and QoS-based web service composition. We model the former problem as a prediction problem while the later problem is modelled as an NP-Hard optimization problem due to its complex, constrained and multi-objective nature. This thesis contributed to the prediction problem by presenting a novel learning automata-based non-negative matrix factorization algorithm (LANMF) for estimating end-to-end network latency of a composition in the cloud. LANMF encodes each web service node as an automaton which allows v it to estimate its network coordinate in such a way that prediction error is minimized. Experiments indicate that LANMF is more accurate than current approaches. The thesis also contributed to the QoS-based service composition problem by proposing four evolutionary algorithms; a network-aware genetic algorithm (INSGA), a K-mean based genetic algorithm (KNSGA), a multi-population particle swarm optimization algorithm (NMPSO), and a non-dominated sort fruit fly algorithm (NFOA). The algorithms adopt different evolutionary strategies coupled with LANMF method to search for low latency and QoSoptimal solutions. They also employ a unique constraint handling method used to penalize solutions that violate user specified QoS constraints. Experiments demonstrate the efficiency and scalability of the algorithms in a large scale environment. Also the algorithms outperform other evolutionary algorithms in terms of optimality and calability. In addition, the thesis contributed to QoS-based web service composition in a dynamic environment. This is motivated by the ineffectiveness of the four proposed algorithms in a dynamically hanging QoS environment such as a real world scenario. Hence, we propose a new cellular automata-based genetic algorithm (CellGA) to address the issue. Experimental results show the effectiveness of CellGA in solving QoS-based service composition in dynamic QoS environment.

Multi-objective optimisation focuses on optimising multiple objectives simultanuously. Evolutionary and immune-based algorithms have been developed in order to solve multi-objective optimisation problems. These algorithms often include a property called elitism, a method of preserving good solutions. This study has focused on how different approaches of elitism affect an algorithm's ability to find optimal solutions in a multi-objective optimisation problem with a discrete and highly discontinuous decision space. Three state-of-the-art algorithms, NSGA-II, SPEA2+ and NNIA2, were implemented, validated and tested against a multi-objective optimisation problem of a miniature plant. Final populations yielded from all the algorithms were included in an analysis. The results of this study indicate that external populations are important in order for algorithms to find optimal solutions in multi-objective optimisation problems with a discrete and highly discontinuous decision spaces.

Les travaux de recherche présentés dans ce mémoire s'inscrivent dans la lignée des approches de modélisation hydrologiques prédictives dirigées par les données. Nous avons particulièrement développé leur application sur le contexte difficile des phénomènes de crue éclairs caractéristiques des bassins versants de la région Caraïbe qui pose un dé fi sé.curi taire. En envisageant le problème de la prévision de crues comme un problème d'optimisation combinatoire difficile nous proposons d'utiliser la notion de métaneuristiques, à travers les algorithmes évolutionnaire notamment pour leur capacité à parcourir efficacement de grands espaces de recherche et fi fournir des solutions de bOlIDe qualité en des temps d'exécution raisonnables. Nous avons présenté l'approche de prédiction AV2D : Aggregate Variable Data Driven dom le concept central est la notion de variable agrégée. L'idée sous-jacente à ce concept est de considérer le pouvoir prédictif de nouvelles variables définies comme le résultat de fonctions tatistiques, dites d'agrégation calculées sur de donnée' correspondant à des périodes de temps précédent uo événem nt à prédire. Ces variable sont caractérisées par des ensembles de paramètres correspondant a leur pJ:opriétés. Nous avons imroduitle variables agrégées hydrométéorologiques permettant de répondre au problème de la classification d événements hydrologiques. La complexité du parcours de l'espace de recherche engendré par les paramètres définissant ces variables a été prise en compte grâce à la njse en oeuvre d'un algorithme évolutionnaire particulier dont les composants ont été spécifiquement définis pour ce problème. Nous avons montré, à travers une étude comparative avec d'autres approches de modélisation dirigées par les données, menée sur deux cas d'études de bassins versant caribéens, que l'approche AV2D est particulièrement bien adaptée à leur contexte. Nous étudions par la suite les bénéfices offerts par les approches de modélisation hydrologiques modulaires dirigées par les données, en définissant un procédé de division en sous-processus prenant en compte les caractéristiques paniculières des bassins versants auxquels nous nous intéressons. Nou avons proposé une extension des travaux précédents à travers la définition d'une approche de modélisation modulaire M2D: Spatial Modular Data Driven, consistant à considérer des sous-processus en divisant l'ensemble des exemples à classifier en sous-ensembles correspondant à des comportements hydrologiques homogènes. Nous avons montré à travers une étude comparative avec d autres approches dU'igées par les données mises en oeuvre sur les mêmes sous-ensembles de données que celte approche permet d améliorer les résultats de prédiction particulièrement à coun Lenne. Nous avons enfin proposé la modélisation d un outil de pi / The work presented in this thesis is in the area of data-driven hydrological modeling approaches. We particularly investigared their application on the difficult problem of flash flood phenomena typically observed in Caribbean watersheds. By considering the problem of flood prediction as a combinatorial optimization problem, we propose to use the notion of Oleraheuristics, through evolutionary algorithms, especially for their capacity ta visit effjciently large search space and to provide good solutions in reasonable execution times. We proposed the hydrological prediction approach AV2D: Aggregate Variable Data Driven which central concept is the notion of aggregate variable. The underlying idea of this [concept is to consider the predictive power of new variables defined as the results of statistical functions, called aggregation functions, computed on data corresponding ta time periods before an event ta predict. These variables are characterized by sets of parameters corresponding ta their specifications. We introduced hydro-meteorological aggregate variables allowing ta address the classification problem of hydrological events. We showed through a comparative study on two typical caribbean watersheds, using several common data driven modelling techniques that the AV2D approach is panicul.rly weil fitted ta the studied context. We also study the benefits offered by modulaI' approaches through the definition of the SM2D: Spatial Modular DataDriven approach, consisting in considering sub-processes partly defined by spatial criteria. We showed that the results obtained by the AV2D on these sub-processes allows to increase the performances particularly for short term prediction. Finally we proposed the modelization of a generic control tool for hydro-meteorological prediction systems, H2FCT: Hydro-meteorological Flood Forecasting Control 1'001