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High Order Contingency Selection using Particle Swarm Optimization and Tabu SearchChegu, Ashwini 01 August 2010 (has links)
There is a growing interest in investigating the high order contingency events that may result in large blackouts, which have been a great concern for power grid secure operation. The actual number of high order contingency is too huge for operators and planner to apply a brute-force enumerative analysis. This thesis presents a heuristic searching method based on particle swarm optimization (PSO) and tabu search to select severe high order contingencies. The original PSO algorithm gives an intelligent strategy to search the feasible solution space, but tends to find the best solution only. The proposed method combines the original PSO with tabu search such that a number of top candidates will be identified. This fits the need of high order contingency screening, which can be eventually the input to many other more complicate security analyses. Reordering of branches of test system based on severity of N-1 contingencies is applied as a pre-processing to increase the convergence properties and efficiency of the algorithm. With this reordering approach, many critical high order contingencies are located in a small area in the whole searching space. Therefore, the proposed algorithm tends to concentrate in searching this area such that the number of critical branch combinations searched will increase. Therefore, the speedup ratio is found to increase significantly. The proposed algorithm is tested for N-2 and N-3 contingencies using two test systems modified from the IEEE 118-bus and 30-bus systems. Variation of inertia weight, learning factors, and number of particles is tested and the range of values more suitable for this specific algorithm is suggested. Although illustrated and tested with N-2 and N-3 contingency analysis, the proposed algorithm can be extended to even higher order contingencies but visualization will be difficult because of the increase in the problem dimensions corresponding to the order of contingencies.
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Optimisation of liquid fuel injection in gas turbine enginesComer, Adam Landon January 2013 (has links)
No description available.
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Models and algorithms for the combinatorial optimization of WLAN-based indoor positioning systemZheng, You 20 April 2012 (has links) (PDF)
Indoor Positioning Systems (IPS) using the existing WLAN have won growing interest in the last years, it can be a perfect supplement to provide location information of users in indoor environments where other positioning techniques such as GPS, are not much effective. The thesis manuscript proposes a new approach to define a WLAN-based indoor positioning system (WLAN-IPS) as a combinatorial optimization problem to guarantee the requested communication quality while optimizing the positioning error. This approach is characterised by several difficult issues we tackled in three steps.At first, we designed a WLAN-IPS and implemented it as a test framework. Using this framework, we looked at the system performance under various experimental constraints. Through these experiments, we went as far as possible in analysing the relationships between the positioning error and the external environmental factors. These relationships were considered as evaluation indicators of the positioning error. Secondly, we proposed a model that defines all major parameters met in the WLAN-IPS from the literature. As the original purpose of the WLAN infrastructures is to provide radio communication access, we introduced an additional purpose which is to minimize the location error within IPS context. Two main indicators were defined in order to evaluate the network Quality of Service (QoS) and the positioning error for Location-Based Service (LBS). Thirdly, after defining the mathematical formulation of the optimisation problem and the key performance indicators, we proposed a mono-objective algorithm and a multi-objective algorithm which are based on Tabu Search metaheuristic to provide good solutions within a reasonable amount of time. The simulations demonstrate that these two algorithms are highly efficient for the indoor positioning optimization problem.
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Heuristiques efficaces pour l'optimisation de la performance des systèmes séries-parallèlesOuzineb, Mohamed January 2009 (has links)
Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal
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Parallel metaheuristics for stochastic capacitated multicommodity network designFu, Xiaorui January 2008 (has links)
Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal
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Optimisation Heuristics for CryptologyClark, Andrew J. January 1998 (has links)
The aim of the research presented in this thesis is to investigate the use of various optimisation heuristics in the fields of automated cryptanalysis and automated cryptographic function generation. These techniques were found to provide a successful method of automated cryptanalysis of a variety of the classical ciphers. Also, they were found to enhance existing fast correlation attacks on certain stream ciphers. A previously proposed attack of the knapsack cipher is shown to be flawed due to the absence of a suitable solution evaluation mechanism. Finally, a new approach for finding highly nonlinear Boolean functions is introduced.
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Proposição e análise de modelos híbridos para o problema de escalonamento de produção em oficina de máquinas / Presentation and analysis of hybridization models for the jobshop scheduling problemTatiana Balbi Fraga 26 March 2010 (has links)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Nas últimas décadas, o problema de escalonamento da produção em oficina de
máquinas, na literatura referido como JSSP (do inglês Job Shop Scheduling Problem), tem
recebido grande destaque por parte de pesquisadores do mundo inteiro. Uma das razões que
justificam tamanho interesse está em sua alta complexidade. O JSSP é um problema de
análise combinatória classificado como NP-Difícil e, apesar de existir uma grande variedade
de métodos e heurísticas que são capazes de resolvê-lo, ainda não existe hoje nenhum método
ou heurística capaz de encontrar soluções ótimas para todos os problemas testes apresentados
na literatura. A outra razão basea-se no fato de que esse problema encontra-se presente no diaa-
dia das indústrias de transformação de vários segmento e, uma vez que a otimização do
escalonamento pode gerar uma redução significativa no tempo de produção e,
consequentemente, um melhor aproveitamento dos recursos de produção, ele pode gerar um
forte impacto no lucro dessas indústrias, principalmente nos casos em que o setor de produção
é responsável por grande parte dos seus custos totais. Entre as heurísticas que podem ser
aplicadas à solução deste problema, o Busca Tabu e o Multidão de Partículas apresentam uma
boa performance para a maioria dos problemas testes encontrados na literatura. Geralmente, a
heurística Busca Tabu apresenta uma boa e rápida convergência para pontos ótimos ou subótimos,
contudo esta convergência é frequentemente interrompida por processos cíclicos e a
performance do método depende fortemente da solução inicial e do ajuste de seus parâmetros.
A heurística Multidão de Partículas tende a convergir para pontos ótimos, ao custo de um
grande esforço computacional, sendo que sua performance também apresenta uma grande
sensibilidade ao ajuste de seus parâmetros. Como as diferentes heurísticas aplicadas ao
problema apresentam pontos positivos e negativos, atualmente alguns pesquisadores
começam a concentrar seus esforços na hibridização das heurísticas existentes no intuito de
gerar novas heurísticas híbridas que reúnam as qualidades de suas heurísticas de base,
buscando desta forma diminuir ou mesmo eliminar seus aspectos negativos. Neste trabalho,
em um primeiro momento, são apresentados três modelos de hibridização baseados no
esquema geral das Heurísticas de Busca Local, os quais são testados com as heurísticas Busca
Tabu e Multidão de Partículas. Posteriormente é apresentada uma adaptação do método
Colisão de Partículas, originalmente desenvolvido para problemas contínuos, onde o método
Busca Tabu é utilizado como operador de exploração local e operadores de mutação são
utilizados para perturbação da solução. Como resultado, este trabalho mostra que, no caso dos
modelos híbridos, a natureza complementar e diferente dos métodos Busca Tabu e Multidão
de Partículas, na forma como são aqui apresentados, da origem à algoritmos robustos capazes
de gerar solução ótimas ou muito boas e muito menos sensíveis ao ajuste dos parâmetros de
cada um dos métodos de origem. No caso do método Colisão de Partículas, o novo algorítimo
é capaz de atenuar a sensibilidade ao ajuste dos parâmetros e de evitar os processos cíclicos
do método Busca Tabu, produzindo assim melhores resultados. / In recent decades, the Job Shop Scheduling Ploblem (JSSP) has received great
attention of researchers worldwide. One of the reasons for such interest is its high complexity.
The JSSP is a combinatorial optimization problem classified as NP-Hard and, although there
is a variety of methods and heuristics that are able to solve it, even today no method or
heuristic is able to find optimal solutions for all benchmarcks presented in the literature. The
other reason builds on noted fact that this problem is present in day-to-day of industries of
various segments and, since the optimal scheduling may cause a significant reduction in
production time and thus a better utilization of manufacturing resources, it can generate a
strong impact on the gain of these industries, especially in cases where the production sector
is responsible for most of their total costs. Among the heuristics that can be applied to the
solution of this problem, the Tabu Search and the Particle Swarm Optimization show good
performance for most benchmarcks found in the literature. Usually, the Taboo Search heuristic
presents a good and fast convergence to the optimal or sub-optimal points, but this
convergence is frequently interrupted by cyclical processes, offset, the Particle Swarm
Optimization heuristic tends towards a convergence by means of a lot of computational time,
and the performance of both heuristics strongly depends on the adjusting of its parameters.
This thesis presents four different hybridization models to solve the classical Job Shop
Scheduling Problem, three of which based on the general schema of Local Search Heuristics
and the fourth based on the method Particle Collision. These models are analyzed with these
two heuristics, Taboo Search and Particle Swarm Optimization, and the elements of this
heuristics, showing what aspects must be considered in order to achieve a best solution of the
one obtained by the original heuristics in a considerable computational time. As results this
thesis demonstrates that the four models are able to improve the robustness of the original
heuristics and the results found by Taboo Search.
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[en] MATHEURISTICS FOR VARIANTS OF THE DOMINATING SET PROBLEM / [pt] MATEURÍSTICAS PARA VARIANTES DO PROBLEMA DO CONJUNTO DOMINANTEMAYRA CARVALHO ALBUQUERQUE 14 June 2018 (has links)
[pt] Esta tese faz um estudo do problema do Conjunto Dominante, um problema NP-difícil de grande relevância em aplicações relacionadas ao projeto de rede sem fio, mineração de dados, teoria de códigos, dentre outras. O conjunto dominante mínimo em um grafo é um conjunto mínimo de vértices de modo que cada vértice do grafo pertence a este conjunto ou é adjacente a um vértice que pertence a ele. Três variantes do problema foram estudadas; primeiro, uma variante na qual considera pesos nos vértices, buscando um conjunto dominante com menor peso total; segundo, uma variante onde o subgrafo induzido pelo conjunto dominante está conectado; e, finalmente, a variante que engloba essas duas características. Para resolver esses três problemas, propõe-se um algoritmo híbrido baseado na meta-heurística busca tabu com componentes adicionais de programação matemática, resultando em um método por vezes chamado de mateurística, (matheuristic, em inglês). Diversas técnicas adicionais e vizinhanças largas foram propostas
afim de alcançar regiões promissoras no espaço de busca. Análises experimentais demonstram a contribuição individual de todos esses componentes. Finalmente, o algoritmo é testado no problema do código de cobertura mínima, que pode ser visto como um caso especial do problema do conjunto dominante. Os códigos são estudados na métrica Hamming e na métrica Rosenbloom-Tsfasman. Neste último, diversos códigos menores foram encontrados. / [en] This thesis addresses the Dominating Set Problem, an NP- hard problem with great relevance in applications related to wireless network design, data mining, coding theory, among others. The minimum dominating set in a graph is a minimal set of vertices so that each vertex of the graph belongs to it or is adjacent to a vertex of this set. We study three variants of the problem: first, in the presence of weights on vertices, searching for a dominating set with smallest total weight; second, a variant where the subgraph induced by the dominating set needs to be connected, and,finally, the variant that encompasses these two characteristics. To solve these three problems, we propose a hybrid algorithm based on tabu search with additional mathematical-programming components, leading to a method sometimes called matheuristic. Several additional techniques and large neighborhoods are also employed to reach promising regions in the search space. Our experimental analyses show the good contribution of all these individual components. Finally, the algorithm is tested on the covering code problem, which can be viewed as a special case of the minimum dominating set problem. The codes are studied for the Hamming metric and the Rosenbloom-Tsfasman metric. For this last case, several shorter codes were found.
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Melhoria na converg?ncia do algoritmo Q-Learning na aplica??o de sistemas tutores inteligentesPaiva, ?verton de Oliveira 16 August 2016 (has links)
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Previous issue date: 2016 / O uso sistemas computacionais como complemento ou substitui??o da sala de aula ? cada vez
mais comum na educa??o e os Sistemas Tutores Inteligentes (STIs) s?o uma dessas alternativas.
Portanto ? fundamental desenvolver STIs capazes tanto de ensinar quanto aprender informa??es
relevantes sobre o aluno atrav?s de t?cnicas de intelig?ncia artificial. Esse aprendizado acontece
por meio da intera??o direta entre o STI e o aluno que ? geralmente demorada. Esta disserta??o
apresenta a inser??o da metaheur?sticas Lista Tabu e GRASP com o objetivo de acelerar esse
aprendizado. Para avaliar o desempenho dessa modifica??o, foi desenvolvido um simulador de
STI. Nesse sistema, foram realizadas simula??es computacionais para comparar o desempenho
da tradicional pol?tica de explora??o aleat?ria e as metaheur?sticas propostas Lista Tabu e
GRASP. Os resultados obtidos atrav?s dessas simula??es e os testes estat?sticos aplicados
indicam fortemente que a introdu??o de meta-heur?sticas adequadas melhoram o desempenho
do algoritmo de aprendizado em STIs. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2016. / Using computer systems as a complement or replacement for the classroom experience is an
increasingly common practice in education and Intelligent Tutoring Systems (ITS) are one of
these alternatives. Therefore, it is crucial to develop ITS that are capable of both teaching and
learning relevant information about the student through artificial intelligence techniques. This
learning process occurs by means of direct, and generally slow, interaction between the ITS and
the student. This dissertation presents the insertion of meta-heuristic Tabu search and GRASP
with the purpose of accelera ting learning. An ITS simulator was developed to evaluate the
performance of this change. Computer simulations were conducted in order to compare the
performance of traditional randomized search methods with the meta-heuristic Tabu search.
Results obtained from these simulations and statistical tests strongly indicate that the
introduction of meta-heuristics in exploration policy improves the performance of the learning
algorithm in ITS.
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Metaheuristic approaches to realistic portfolio optimisationBusetti, Franco Raoul 06 1900 (has links)
In this thesis we investigate the application of two heuristic methods, genetic
algorithms and tabu/scatter search, to the optimisation of realistic portfolios. The
model is based on the classical mean-variance approach, but enhanced with floor and
ceiling constraints, cardinality constraints and nonlinear transaction costs which
include a substantial illiquidity premium, and is then applied to a large I 00-stock
portfolio.
It is shown that genetic algorithms can optimise such portfolios effectively and within
reasonable times, without extensive tailoring or fine-tuning of the algorithm. This
approach is also flexible in not relying on any assumed or restrictive properties of the
model and can easily cope with extensive modifications such as the addition of
complex new constraints, discontinuous variables and changes in the objective
function.
The results indicate that that both floor and ceiling constraints have a substantial
negative impact on portfolio performance and their necessity should be examined
critically relative to their associated administration and monitoring costs.
Another insight is that nonlinear transaction costs which are comparable in magnitude
to forecast returns will tend to diversify portfolios; the effect of these costs on
portfolio risk is, however, ambiguous, depending on the degree of diversification
required for cost reduction. Generally, the number of assets in a portfolio invariably
increases as a result of constraints, costs and their combination.
The implementation of cardinality constraints is essential for finding the bestperforming
portfolio. The ability of the heuristic method to deal with cardinality
constraints is one of its most powerful features. / Decision Sciences / M. Sc. (Operations Research)
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