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.

Orientador: Maria Gabriela Caffarena Celani / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo / Made available in DSpace on 2018-08-27T01:58:13Z (GMT). No. of bitstreams: 1 Martino_JarryerAndradede_D.pdf: 15987793 bytes, checksum: e3e7fece0c549d866ab2fb31f75bd0c8 (MD5) Previous issue date: 2015 / Resumo: O projeto de arquitetura é composto por diferentes variáveis que precisam ser constantemente negociadas, algumas delas envolvem situações contraditórias, aumentando a complexidade da solução do problema. Os algoritmos evolutivos correspondem a um conjunto de técnicas que contribuem para a solução desse tipo de problema, e que podem ser incorporados ao sistema generativo de projeto de maneira a potencializar a obtenção de melhores resultados. Para isso, foi necessário entender a teoria evolucionista e os seus principais mecanismos, a estruturação e a implementação dos algoritmos evolutivos no ambiente computacional, e a sistematização do processo de projeto como base para o desenvolvimento de um método evolutivo. Dessa forma, foi definido um quadro teórico composto pelos principais eventos e conceitos relacionados à teoria evolucionista, à computação evolutiva e à discussão na década de 1960 sobre a sistematização do processo de projeto como uma sequência operativa capaz de registrar o processo mental do projetista, e o método evolutivo de projeto de arquitetura, sendo apresentada a sua estrutura, os componentes e exemplos. Embora esse método tivesse sido implementado na arquitetura na década de 1960, foi verificado que as aplicações estavam bastante restritas, limitando-se a trabalhos acadêmicos em universidades específicas. O domínio de uma linguagem de programação e a falta de clareza e apropriação dos vocabulários, conceitos e técnicas desenvolvidas pela Computação Evolutiva dificultaram a sua implementação como método de projeto na arquitetura e urbanismo. Atualmente, existem recursos digitais que facilitam a implementação desse método de maneira simplificada sem perder a eficiência do método, justificando a sua retomada como um método de projeto pelos arquitetos e urbanistas. Dessa forma, os objetivos desta pesquisa foram os de organizar o conteúdo teórico dos algoritmos evolutivos de maneira a esclarecer a sua estrutura, o vocabulário, os conceitos básicos e os mecanismos que os envolvem, de definir como ocorre a sua relação com o elemento arquitetônico e com o método de projeto, da identificação de uma ferramenta computacional capaz de facilitar a sua implementação e o de apresentar situações concretas em que os arquitetos e urbanistas possam utilizá-los. Como resultado foi possível verificar que não existe dificuldade no entendimento do mecanismo evolutivo como possível recurso para o desenvolvimento de um método de projeto, mas sim, a necessidade de maior domínio sobre a ferramenta de programação que não estaria relacionada diretamente com o sistema evolutivo, mas sim, com a descrição algorítmica através de um código computacional de todo processo de projeto / Abstract: The architecture design is composed by different variables that need to be negotiated, some of them involve contradictory situations, increasing the complexity of the solution. The evolutionary algorithms are set by techniques that contribute to obtain solutions for this kind of problems, and they also may be incorporated in a project generative system in a way that potentiate the best results obtaining. For this it was necessary to understand the evolutionary theory and its main mechanisms, the structuring and implementation of evolutionary algorithms in computational environment, and the systematization of the design process as a base of an evolutionary design method development. Thus, it was important to define a theoretical framework from the main events and concepts related to the evolutionary theory, the evolutionary computation and to the discussion in the 1960s about the systematization of the design process as an operative sequence capable of registering the mental process of the designer and the evolutionary design method on architecture with their components and examples. Although this method had been implemented in architecture in the 1960s, its application was quite restricted to academic works development in some specific universities. The necessity of the knowledge of programming language, vocabulary, concepts and techniques from evolutionary computation made the implementation difficult as a project method in architecture and urbanism. Currently, there are digital resources that facilitate the method simplified implementation without losing its efficiency, justifying its resumption as a design method by architects and urban planners. Moreover, the objectives of this research were to organize the content about evolutionary algorithms, clarifying its structure, vocabulary, basic concepts and the involved mechanisms, to define its relationship with the architectural element and the project method, to identify a computational tool that facilitates the implementation and to present the real situations which architects can use them. As a result it was possible to validate that there is no difficulty in understanding the evolutionary algorithm as possible resource for the methodology development of a design, yet, the necessity to have more experience in the utilization of programming tool. This tool is not directly related to the evolutionary system, but with the algorithmic description through the computational implementation by any project codes / Doutorado / Arquitetura, Tecnologia e Cidade / Doutor em Arquitetura, Tecnologia e Cidade

A predição de estruturas de proteínas (Protein Structure Prediction PSP) é um problema computacionalmente complexo. Para tratar esse problema, modelos simplificados de proteínas, como o Modelo HP, têm sido empregados para representar as conformações e Algoritmos Evolutivos (AEs) são utilizados na busca por soluções adequadas para PSP. Entretanto, abordagens utilizando AEs muitas vezes não tratam adequadamente as soluções geradas, prejudicando o desempenho da busca. Neste trabalho, é apresentada uma formulação multiobjetivo para PSP em Modelo HP, de modo a avaliar de forma mais robusta as conformações produzidas combinando uma avaliação baseada no número de contatos hidrofóbicos com a distância entre os monômeros. Foi adotado o Algoritmo Evolutivo Multiobjetivo em Tabelas (AEMT) a fim de otimizar essas métricas. O algoritmo pode adequadamente explorar o espaço de busca com pequeno número de indivíduos. Como consequência, o total de avaliações da função objetivo é significativamente reduzido, gerando um método para PSP utilizando Modelo HP mais rápido e robusto / Protein Structure Prediction (PSP) is a computationally complex problem. To overcome this drawback, simplified models of protein structures, such as the HP Model, together with Evolutionary Algorithms (EAs) have been investigated in order to find appropriate solutions for PSP. EAs with the HP Model have shown interesting results, however, they do not adequately evaluate potential solutions by using only the usual metric of hydrophobic contacts, hamming the performance of the algorithm. In this work, we present a multi-objective approach for PSP using HP Model that performs a better evaluation of the solutions by combining the evaluation based on the number of hydrophobic contacts with the distance among the hydrophobic amino acids. We employ a Multi-objective Evolutionary Algorithm based on Sub-population Tables (MEAT) to deal with these two metrics. MEAT can adequately explore the search space with relatively low number of individuals. As a consequence, the total assessments of the objective function is significantly reduced generating a method for PSP using HP Model that is faster and more robust

The increasing worldwide demand for software systems involved in society has led to the need where not only functionality is fundamental and addressed, but end-user satisfaction in terms of availability, throughput, and response time is essential and should be preserved. Thus, systems must be evaluated at preset load levels to assess the non-functional quality problems from the closest perspective of real application use. In this context, where the problem involves a high and complex search space, a search-based approach for load test generation is required. This thesis proposes and evaluates an evolutionary search-based approach for load test generation using multi-objective optimization methods consisting of selection, crossover, and mutation operators. In this thesis, load testing is addressed as a multi-objective optimization problem by using four different evolutionary algorithms: Non-dominated Storing Genetic Algorithm II (NSGA-II), Pareto Archived Evolution Strategy (PAES), The Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multi-Objective Cellular Genetic Algorithm (MOCell) as well as a Random Search algorithm. Additionally, this study demonstrates the applicability of the proposed approach by running several experiments, aiming to compare the algorithms’ efficiency based on different quality indicators such as hypervolume, spread, and epsilon. Experimental results show that evolutionary search-based methods can be used to generate effective workloads. Since, all algorithms have found the optimal workload, having the hypervolume values to zero, we believe that the objectives of the problem could be combined as a single objective, hence scalarization techniques can be applicable. Based on the other quality indicators (Spread and Epsilon respectively), NSGA-II and MOCell tend to perform better compared to other algorithms. Finally, the study concludes that multi-objective evolutionary algorithms can be used for load testing purpose, obtaining better results in generating optimal workloads than an existing (adapted) model-free reinforcement learning approach.

Nowadays, the traffic tomography represents an integral component in converged networks and systems for detecting their behavioral characteristics. The dissertation deals with research of its implementation with the use of evolutionary algorithms. The research was mainly focused on innovation and solving behavioral detection data flows in networks and network anomalies using tomography and evolutionary algorithms. Within the dissertation has been proposed a new algorithm, emerging from the basics of the statistical method survival analysis, combined with a genetics’ algorithm. The proposed algorithm was tested in a model of a self-created network probe using the Python programming language and Cisco laboratory network devices. Performed tests have shown the basic functionality of the proposed solution.

This thesis will deal with the use of artificial intelligence methods for solving optimization problems with multiple variables. A theorethical part presents problems of global optimization and overview of solution methods. For practical reasons, special attention is paid to evolutionary algorithms. The subject of optimization itself is energy harvester based on a piezoelectric effect. Its nature and modeling is devoted to one chapter. A part of the thesis is the implementation of the SOMA algorithm for finding the optimal parameters of the generator for maximum performance.

The aim of this work is to design and implement a program for automated design of convolutional neural networks (CNN) with the use of evolutionary computing techniques. From a practical point of view, this approach reduces the requirements for the human factor in the design of CNN architectures, and thus eliminates the tedious and laborious process of manual design. This work utilizes a special form of genetic programming, called Cartesian genetic programming, which uses a graph representation for candidate solution encoding.This technique enables the user to parameterize the CNN search process and focus on architectures, that are interesting from the view of used computational units, accuracy or number of parameters. The proposed approach was tested on the standardized CIFAR-10dataset, which is often used by researchers to compare the performance of their CNNs. The performed experiments showed, that this approach has both research and practical potential and the implemented program opens up new possibilities in automated CNN design.

Real-time strategy games are an exciting area of research, as creating a game AI poses many challenges - from managing a single unit to completing an objective of the game. This thesis explores possible solutions to this task, using genetic programming and neuroevolution. It presents and compares findings and differences between the models. Both methods performed reasonably well, but genetic programming was found to be a bit more effective in performance and results.

This paper deals with the problem of optimization of a production plan by using genetic algorithms. It contains a brief overview of the principles behind genetic algorithms in scope of evolutionary algorithms and artificial intelligence in general. It also takes a closer look on the challenge of production planning and control and all activities connected to it. This is followed by description of the modification of genetic algorithms that needed to be done in order to implement it into a computer program, which is used to create and optimize the production plan, and is a result to the issue that this paper deals with. Incorporated is detailed escription of principles and functions of the program, that it offers to its users.

Dizertační práce pojednává o návrhu a optimalizaci periodických struktur s elektromagnetickým zádržným pásmem (EBG – electromagnetic band gap) pro potlačení povrchových vln šířících se na elektricky tlustých dielektrických substrátech. Nepředvídatelné chování elektromagnetických vlastností těchto struktur v závislosti na parametrech elementární buňky činí jejích syntézi značně komplikovanou. Bez patřičného postupu je návrh EBG struktur metodou pokusu a omylu. V první části práce jsou shrnuty základní poznatky o šíření elektromagnetických vln v tzv. metamateriálech. Následně je diskutován správný způsob výpočtu disperzního diagramu ve vybraných komerčních programech. Jádrem dizertace je automatizovaný návrh a optimalizace EBG struktur využitím různých globálních optimalizačních algoritmů. Praktický význam vypracované metodiky je předveden na návrhových příkladech periodických struktur s redukovanými rozměry, dvoupásmovými EBG vlastnostmi, simultánním EBG a AMC (artificial magnetic conductor – umělý magnetický vodič) chováním a tzv. superstrátu. Poslední kapitola je věnována experimentálnímu ověření počítačových modelů.