There are many combinatorial optimization problems such as traveling salesman problem, quadratic-assignment problem, flow shop scheduling, that are computationally intractable. Tabu search based simulated annealing is a stochastic search algorithm that is widely used to solve combinatorial optimization problems. Due to excessive run time, there is a strong demand for a parallel version that can be applied to any problem with minimal modifications. Existing advanced and/or parallel versions of tabu search algorithms are specific to the problem at hand. This leads to a drawback of optimization only for that particular problem. In this work, we propose a parallel version of tabu search based SA on the Graphics Processing Unit (GPU) platform. We propose two variants of the algorithm based on where the tabu list is stored (global vs. local). In the first version, the list is stored in the global shared memory such that all threads can access this list. Multiple random walks in solution space are carried out. Each walk avoids the moves made in rest of the walks due to their access to global tabu list at the expense of more time. In the second version, the list is stored at the block level and is shared by only the block threads. Groups of random walks are performed in parallel and a walk in a group avoids the moves made by the rest of the walks within that group due to their access to shared local tabu list. This version is better than the first version in terms of execution time. On the other hand, the first version finds the global optima more often. We present experimental results for six difficult optimization functions with known global optima. Compared to the CPU implementation with similar workload, the proposed GPU versions are faster by approximately three orders of magnitude and often find the global optima.

Fundamental to the electronic security of information and communication systems, is the correct use and application of appropriate ciphers. The strength of these ciphers, particularly in their ability to resist cryptanalytic attacks, directly in uences the overall strength of the entire system. The strength of the underlying cipher is reliant upon a robust structure and the carefully designed interaction between components in its architecture. Most importantly, however, cipher strength is critically dependent on the strength of the individual components of which it is comprised. Boolean functions and substitution boxes (s-boxes) are among the most common and essential components of ciphers. This is because they are able to provide a cipher with strengthening properties to resist known and potential cryptanalytic attacks. Thus, it is not surprising that significant research effort has been made in trying to develop ways of obtaining boolean functions and substitution boxes with optimal achievable measures of desirable cryptographic properties. Three of the main cryptographic properties required by strong boolean functions and s-boxes are nonlinearity, correlation immunity and propagation criteria, with different cryptographic applications requiring different acceptable measures of these and other properties. As combinations of cryptographic properties exhibited by functions can be conicting, finding cryptographically strong functions often means that a trade-off needs to be made when optimizing property values. Throughout this thesis, the term "optimization" specifically refers to seeking to obtain the best achievable combination of target property values which may be exhibited by boolean functions and s-boxes, regardless of whether the relevant properties are conflicting or complementary. This thesis focusses on a particular class of techniques for obtaining strong functions for cryptographic applications, referred to as heuristic methods or, simply, heuristics. Three new heuristic methods, each aimed at generating boolean functions optimizing one or more of the main cryptographic properties mentioned above, in addition to other desirable properties, are presented. The first of the new heuristic methods developed for this thesis focusses on generating boolean functions which are balanced and exhibit very high nonlinearities. Highly nonlinear balanced functions are critical to many cryptographic applications, as they provide good resistance to linear cryptanalytic attacks. This first method is based on the recursive modification of a starting bent function and is shown to be highly successful and efficient at generating numerous such functions, which also exhibit low autocorrelation values, in a very short computational time. The generation of balanced, correlation immune boolean functions that also exhibit the confl icting property of high nonlinearity is the focus of the second new heuristic method developed for this thesis. By concatenating selected pairs of lower-dimensional boolean functions together in the Walsh Hadamard transform domain, direct optimization for both resilience and nonlinearity was able to take place at each level towards and for the final function. This second method was able to generate examples of boolean functions with almost all of the best known optimal combinations of target property values. Experiments have shown the success of this method in consistently generating highly nonlinear resilient boolean functions, for a range of orders of resilience, with such functions possessing optimal algebraic degree. A third new heuristic method, which searches for balanced boolean functions which satisfy a non-zero degree of propagation criteria and exhibit high nonlinearity, is presented. Intelligent bit manipulations in the truth table of starting functions, based on fundamental relationships between boolean function transforms and measures, provide the design rationale for this method. Two new function generation schemes have been proposed for this method, to efficiently satisfy the requirements placed on the starting functions utilized in the computational process. An optional process attempts to increase the algebraic degree of the resulting functions, without sacrificing the optimalities that are achievable. The validity of this method is demonstrated through the success of various experimental trials. Switching the focus from single output boolean functions to multiple output boolean functions (s-boxes), the effectiveness of existing heuristic techniques (namely Genetic Algorithm, Hill Climbing Method and combined Genetic Algorithm/Hill Climbing) in primarily being applied to improve the nonlinearity of s-boxes of various dimensions, is investigated. The prior success of these heuristic techniques for improving the nonlinearity of boolean functions has been previously demonstrated, as has the success of hill climbing in isolation when applied to bijective s-boxes. An extension to the bijective s-box optimization work is presented in this thesis. In this new research, a Genetic Algorithm, Hill Climbing Method and the two in combination are applied to the nonlinearity and autocorrelation optimization of regular NxM s-boxes (N > M) to investigate the effectiveness and efficiency of each of these heuristics. A new breeding scheme, utilized in the Genetic Algorithm and combined Genetic Algorithm/Hill Climbing trials, is also presented. The success of experimental results compared to random regular s-box generation is demonstrated. New research in applying the Hill Climbing Method to construct NxM sboxes (N > M) required to meet specific property criteria is presented. The consideration of the characteristics desired by the constructed s-boxes largely dictated the generation process. A discussion on the generation process of the component functions is included. Part of the results produced by experimental trials were incorporated into a commonly used family of stream ciphers, thus further supporting the use of heuristic techniques as a useful means of obtaining strong functions suitable for incorporation into practical ciphers. An analysis of the cryptographic properties of the s-box used in the MARS block cipher, the method of generation and the computational time taken to obtain this s-box, led to the new research reported in this thesis on the generation of MARS-like s-boxes. It is shown that the application of the Hill Climbing Method, with suitable requirements placed on the component boolean functions, was able to generate multiple MARS-like s-boxes which satisfied the MARS sbox requirements and provided additional properties. This new work represented an alternative approach to the generation of s-boxes satisfying the MARS sbox property requirements but which are cryptographically superior and can be obtained in a fraction of the time than that which was taken to produce the MARS s-box. An example MARS-like s-box is presented in this thesis. The overall value of heuristic methods in generating strong boolean functions and substitution boxes is clearly demonstrated in this thesis. This thesis has made several significant contributions to the field, both in the development of new, specialized heuristic methods capable of generating strong boolean functions, and in the analysis and optimization of substitution boxes, the latter achieved through applying existing heuristic techniques.

Made available in DSpace on 2014-06-11T19:23:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2007-09-06Bitstream added on 2014-06-13T18:51:01Z : No. of bitstreams: 1 ferreira_ab_me_ilha.pdf: 5542320 bytes, checksum: ac4ab4f7279192ce563639cce31eb895 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Desenvolveu-se neste trabalho a implementação computacional de um algoritmo genético. Este se constituiu de uma população inicial sobre a qual agem quatro operadores fundamentais: seleção, “crossover”, substituição e mutação, e produz uma nova população. Sobre a qual agem novamente os operadores genéticos, e assim sucessivamente produzindo uma seqüência de populações. O operador seleção foi implementado em três algoritmos básicos: roda da roleta, amostragem estatística universal e torneio. O “crossover” também foi desenvolvido em algumas opções: um ponto, dois pontos, múltiplos pontos, e uniforme. A substituição de indivíduos da população pelos filhos ocorre de três maneiras básicas: dos pais, dos menos aptos, e dos indivíduos sorteados aleatoriamente. A mutação ocorre de apenas uma maneira. Inicialmente, o algoritmo genético foi executado em computador de maneira seqüencial. Resolveu-se um conjunto de problemas de otimização multidimensional e também o Problema do Caixeiro Viajante (TSP – Traveler Salesman Problem). Fez-se um estudo paramétrico dos vários parâmetros que aparecem no algoritmo genético, tais como: tamanho da população, número de gerações, taxa de seleção, probabilidade de mutação, e taxa de elitismo. No caso de problemas de otimização multidimensional a representação do cromossomo de cada indivíduo é binária, já no caso do TSP a representação é inteira decimal. Em ambos os casos da otimização multidimensional e do TSP também foi utilizada a técnica de hill-climbing visando aumentar a taxa de convergência da solução. A técnica de janelamento foi utilizada somente no caso de otimização multidimensional, também visando aumentar a taxa de convergência. Posteriormente, o algoritmo genético foi executado também em processamento computacional paralelo,... / It was developed in this work the computational implementation of a genetic algorithm. That is constituted of an initial population upon which act four basic operators: selection, crossover, substitution and mutation, producing a new population. Upon which act again the genetic operators, and thus, successively, producing a sequence of populations. The operator selection was implemented in three basic algorithms: roulette wheel, stochastic universal sampling, and tournament. The crossover also was developed in some options: one point, two points, several points, and uniform. Substitution of individuals from the population by the newborns happens in three basic ways: the fathers, the less apt, and the individuals sorted randomly. Mutation happens in only one manner. Initially, the genetic algorithm was processed sequentially in the computer. It was solved a set of multidimensional optimization problems and also the Traveler Salesman Problem - TSP. It was done a parametric study of the several parameters that appear in the genetic algorithm, such as: population size, number of generations, selection rate, mutation probability, and elitism rate. In the case of multidimensional optimization problems the chromosome representation of each individual is binary, but in the case of TSP the representation is integer decimal. In both cases of multidimensional optimization and TSP also it were used the hill-climbing technique aiming to increase the solution convergence rate. The windowing technique was used just for the multidimensional optimization case, also aiming to increase the convergence rate. Lately, the genetic algorithm was also performed in a computational parallel processing mode, using several computers linked by a net. In each computer it was executed one genetic algorithm upon a local population. The interaction among several populations was done through the migration ...(Complete abstract, click electronic access below)

Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-10T18:13:40Z No. of bitstreams: 2 Dissertacao Felipe Borgiani.pdf: 3365746 bytes, checksum: fbfcefa352bee971420dbb93de7d9b5a (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-11T17:39:59Z (GMT). No. of bitstreams: 2 Dissertacao Felipe Borgiani.pdf: 3365746 bytes, checksum: fbfcefa352bee971420dbb93de7d9b5a (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-09-06 / O trânsito caótico das grandes cidades, especialmente em países em desenvolvimento, impacta diretamente na qualidade de vida dos cidadãos, principalmente daqueles que necessitam diariamente dos sistemas de transporte público rodoviários. Diversas abordagens para amenizar esses problemas vem sido apresentadas por pesquisadores do mundo todo, na forma de diferentes propostas de sistemas de transporte inteligentes. Tendo em vista o desenvolvimento tecnológico dos últimos anos, aliado à expansão e popularização do uso de dispositivos computacionais portáteis como smartphones e tablets, este trabalho propõe-se a apresentar um sistema, componente do Sistema de Transporte Inteligente UbiBus, denominado Pilgrim, capaz de gerar e classificar rotas de ônibus em tempo real, e seja sensível ao contexto que permeia o sistema de transporte público rodoviário, em especial o trânsito. Para tanto, serão propostas duas abordagens utilizando técnicas de otimização da inteligência artificial, Algoritmos Genéticos e Hill-Climbing com Reinício Aleatório, para o desenvolvimento do sistema, e apresentadas a arquitetura, a modelagem e os detalhes da implementação. Este trabalho ainda apresenta os experimentos realizados para avaliar o desempenho de ambas as abordagens, comparando-as, e também uma pesquisa feita com potenciais usuários do sistema UbiBus, com o objetivo de avaliar a qualidade das rotas geradas pelo sistema Pilgrim.

Achieving optimal performance of a database can be crucial for many businesses, and tuning its configuration parameters is a necessary step in this process. Many existing tuning methods involve complex machine learning algorithms and require large amounts of historical data from the system being tuned. However, training machine learning models can be problematic if a considerable amount of computational resources and data storage is required. This paper investigates the possibility of using less complex search algorithms or evolutionary algorithms to tune database configuration parameters, and presents a framework that employs Hill Climbing and Particle Swarm Optimization. The performance of the algorithms are tested on a PostgreSQL database using read-only workloads. Particle Swarm Optimization displayed the largest improvement in query response time, improving it by 26.09% compared to using the configuration parameters' default values. Given the improvement shown by Particle Swarm Optimization, evolutionary algorithms may be promising in the field of database tuning.

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Realistically, organizational and/or system efficiency performance is dynamic, non-linear, and a function of multiple interactions in production. However, in the efficiency literature, system performance is frequently evaluated considering linear combinations of the input/output variables, without explicitly taking into account the interactions and feedback mechanisms that explain the causes of efficiency behavior, the dynamic nature of production, and non-linear combinations of the input/output variables. Consequently, policy decisions based on these results may be sub-optimized because the non-linear relationships among variables, causal relationships, and feedback mechanisms are ignored. This research takes the initial steps of evaluating system efficiency performance in a dynamic environment, by relating the factors that effect system efficiency performance to the policies that govern it. First, this research extends the concepts of the static production axioms into a dynamic realm, where inputs are not instantaneously converted into outputs. The relationships of these new dynamic production axioms to the basic behaviors associated with system dynamics structures are explored. Second, this research introduces a methodological approach that combines system dynamics modeling with the measurement of productive efficiency. System dynamics is a modeling paradigm that evaluates system policies by exploring the causal relationships of the important elements within the system. This paradigm is coupled with the fundamental assumptions of production theory in order to evaluate the productive efficiency of a production system operating within a dynamic and non-linear environment. As a result, a subsystem within the system dynamics model is introduced that computes efficiency scores based on the fundamental notions of productive efficiency. The framework's ability to combine prescriptive and descriptive modeling characteristics, as well as dynamic and combinatorial complexity, can potentially have a greater impact on policy decisions and how they affect system efficiency performance. Finally, the utility of these concepts is demonstrated in an implementation case study. This methodology generates a prescriptive dynamical production frontier which defines the optimal production resources required to satisfy system requirements. Additionally, the dynamical production frontier allows for analysis for comparisons between options during a transient period, insight into possible unintended consequences, and the ability to forecast optimal times for introducing system or process improvements. / Ph. D.

Orientador: João Batista Aparecido / Banca: Emanuel Rocha Woiski / Banca: Luis Carlos de Castro Santos / Resumo: Desenvolveu-se neste trabalho a implementação computacional de um algoritmo genético. Este se constituiu de uma população inicial sobre a qual agem quatro operadores fundamentais: seleção, "crossover", substituição e mutação, e produz uma nova população. Sobre a qual agem novamente os operadores genéticos, e assim sucessivamente produzindo uma seqüência de populações. O operador seleção foi implementado em três algoritmos básicos: roda da roleta, amostragem estatística universal e torneio. O "crossover" também foi desenvolvido em algumas opções: um ponto, dois pontos, múltiplos pontos, e uniforme. A substituição de indivíduos da população pelos filhos ocorre de três maneiras básicas: dos pais, dos menos aptos, e dos indivíduos sorteados aleatoriamente. A mutação ocorre de apenas uma maneira. Inicialmente, o algoritmo genético foi executado em computador de maneira seqüencial. Resolveu-se um conjunto de problemas de otimização multidimensional e também o Problema do Caixeiro Viajante (TSP - Traveler Salesman Problem). Fez-se um estudo paramétrico dos vários parâmetros que aparecem no algoritmo genético, tais como: tamanho da população, número de gerações, taxa de seleção, probabilidade de mutação, e taxa de elitismo. No caso de problemas de otimização multidimensional a representação do cromossomo de cada indivíduo é binária, já no caso do TSP a representação é inteira decimal. Em ambos os casos da otimização multidimensional e do TSP também foi utilizada a técnica de hill-climbing visando aumentar a taxa de convergência da solução. A técnica de janelamento foi utilizada somente no caso de otimização multidimensional, também visando aumentar a taxa de convergência. Posteriormente, o algoritmo genético foi executado também em processamento computacional paralelo, ...(Resumo completo, clicar acesso eletrônico abaixo) / Abstract: It was developed in this work the computational implementation of a genetic algorithm. That is constituted of an initial population upon which act four basic operators: selection, crossover, substitution and mutation, producing a new population. Upon which act again the genetic operators, and thus, successively, producing a sequence of populations. The operator selection was implemented in three basic algorithms: roulette wheel, stochastic universal sampling, and tournament. The crossover also was developed in some options: one point, two points, several points, and uniform. Substitution of individuals from the population by the newborns happens in three basic ways: the fathers, the less apt, and the individuals sorted randomly. Mutation happens in only one manner. Initially, the genetic algorithm was processed sequentially in the computer. It was solved a set of multidimensional optimization problems and also the Traveler Salesman Problem - TSP. It was done a parametric study of the several parameters that appear in the genetic algorithm, such as: population size, number of generations, selection rate, mutation probability, and elitism rate. In the case of multidimensional optimization problems the chromosome representation of each individual is binary, but in the case of TSP the representation is integer decimal. In both cases of multidimensional optimization and TSP also it were used the hill-climbing technique aiming to increase the solution convergence rate. The windowing technique was used just for the multidimensional optimization case, also aiming to increase the convergence rate. Lately, the genetic algorithm was also performed in a computational parallel processing mode, using several computers linked by a net. In each computer it was executed one genetic algorithm upon a local population. The interaction among several populations was done through the migration ...(Complete abstract, click electronic access below) / Mestre

Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2017-11-17T16:18:59Z No. of bitstreams: 2 Dissertação - Denner Monteiro de Carvalho - 2017.pdf: 49512193 bytes, checksum: dce0c13146977219335b29250e4960d7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-11-20T10:11:34Z (GMT) No. of bitstreams: 2 Dissertação - Denner Monteiro de Carvalho - 2017.pdf: 49512193 bytes, checksum: dce0c13146977219335b29250e4960d7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-11-20T10:11:34Z (GMT). No. of bitstreams: 2 Dissertação - Denner Monteiro de Carvalho - 2017.pdf: 49512193 bytes, checksum: dce0c13146977219335b29250e4960d7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-09-28 / The present work presents the development of a computational tool for the optimization of processes in the elaboration of electromechanical projects of conventional electricity distribution networks. The current process of elaboration of distribution network projects that has significant topographical interferences is slow and depends on software with a technological lag that requires post processing of the data to generate the final project, besides demanding specific technical knowledge and too much elaboration time. This elaboration time, as well as the errors occurred in the post-processing stage, hinder the approval process of the projects in the electric power concessionaires, and delay the connection of final consumers. For modeling the process, the typologies of the terrain where the network path is to be determined through surveys of the topographic data are verified first. Afterwards, mechanical forces and stresses are observed in each type of electromechanical structure, defining the minimum mounting distances in compliance with related technical norms, as well as incident variables such as wind speed and mechanical variations of the conductors during the operation. The modeling of the decision variables is applied a recursive heuristic method with the application of the Hill Climbing optimization method, which sweeps the entire line defined by topography, performing the mechanical evaluations and calculations with the optimization of technical budget criteria, generating uniform solutions and Automated. The tool obtained is developed in Web platform with friendly interface and updated, and can be used through any browser. The software, besides providing the elaboration of the project with embedded normative criteria, can be easily used for project analysis, providing integrated visualization of the planialtimetric profiles through Google Maps, optimizing the process of visual evaluation. / O presente trabalho apresenta o desenvolvimento de um método computacional destinado à otimização de processos na elaboração de projetos eletromecânicos de redes de distribuição convencional de energia elétrica. O processo atual de elaboração de projetos de redes de distribuição que possui interferências topográficas é lento e depende de softwares com defasagem tecnológica que necessitam de pós processamento dos dados para gerar o projeto final, além de demandar conhecimento técnico específico e demasiado tempo de elaboração. Esse tempo de elaboração, bem como os erros ocorridos na etapa de pós processamento, dificultam o processo de aprovação dos projetos nas concessionárias de energia elétrica, e atrasam a ligação dos consumidores finais. Para modelagem do processo, são verificados primeiramente as tipologias do terreno onde se deseja definir o caminhamento da rede através de levantamentos dos dados topográficos. Após, são observados os esforços e solicitações mecânicas pontualmente em cada tipo de estrutura eletromecânica, definindo as distâncias mínimas de montagem em observância às normas técnicas relacionadas, e também, variáveis incidentes como a velocidade do vento e variações mecânicas dos condutores durante a operação. Realizada a modelagem das variáveis de decisão é aplicado um método heurístico recursivo com a aplicação do método de otimização Hill Climbing, que varre toda a linha definida pela topografia, realizando as avaliações e cálculos mecânicos com a otimização de critérios técnicos orçamentários, gerando soluções uniformizadas e automatizadas. O método computacional é desenvolvido em plataforma Web com interface amigável e atualizada, podendo ser utilizada através de um navegador qualquer. O software além de proporcionar a elaboração do projeto com critérios normativos embutidos, pode ser facilmente utilizada para análise de projetos, proporcionando visualização integrada dos perfis planialtimétricos através Google Maps, otimizando o processo de avaliação visual.

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