Multimodal estimation of distribution algorithms

Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima

A Comparative Study of EAG and PBIL on Large-Scale Global Optimization Problems

Estimation of Distribution Algorithms (EDAs) use global statistical information effectively to sample offspring disregarding the location information of the locally optimal solutions found so far. Evolutionary Algorithm with Guided Mutation (EAG) combines global statistical information and location information to sample offspring, aiming that this hybridization improves the search and optimization process. This paper discusses a comparative study of Population-Based Incremental Learning (PBIL), a representative of EDAs, and EAG on large-scale global optimization problems. We implemented PBIL and EAG to build an experimental setup upon which simulations were run. The performance of these algorithms was analyzed in terms of solution quality and computational cost. We found that EAG performed better than PBIL in attaining a good quality solution, but the latter performed better in terms of computational cost. We also compared the performance of EAG and PBIL with MA-SW-Chains, the winner of CEC'2010, and found that the overall performance of EAG is comparable to MA-SW-Chains

A Comparative Study of EAG and PBIL on Large-Scale Global Optimization Problems

Estimation of Distribution Algorithms (EDAs) use global statistical information effectively to sample offspring disregarding the location information of the locally optimal solutions found so far. Evolutionary Algorithm with Guided Mutation (EAG) combines global statistical information and location information to sample offspring, aiming that this hybridization improves the search and optimization process. This paper discusses a comparative study of Population-Based Incremental Learning (PBIL), a representative of EDAs, and EAG on large-scale global optimization problems. We implemented PBIL and EAG to build an experimental setup upon which simulations were run. The performance of these algorithms was analyzed in terms of solution quality and computational cost. We found that EAG performed better than PBIL in attaining a good quality solution, but the latter performed better in terms of computational cost. We also compared the performance of EAG and PBIL with MA-SW-Chains, the winner of CEC’2010, and found that the overall performance of EAG is comparable to MA-SW-Chains

Scalable parallel evolutionary optimisation based on high performance computing

Evolutionary algorithms (EAs) have been successfully applied to solve various challenging optimisation problems. Due to their stochastic nature, EAs typically require considerable time to find desirable solutions; especially for increasingly complex and large-scale problems. As a result, many works studied implementing EAs on parallel computing facilities to accelerate the time-consuming processes. Recently, the rapid development of modern parallel computing facilities such as the high performance computing (HPC) bring not only unprecedented computational capabilities but also challenges on designing parallel algorithms. This thesis mainly focuses on designing scalable parallel evolutionary optimisation (SPEO) frameworks which run efficiently on the HPC. Motivated by the interesting phenomenon that many EAs begin to employ increasingly large population sizes, this thesis firstly studies the effect of a large population size through comprehensive experiments. Numerical results indicate that a large population benefits to the solving of complex problems but requires a large number of maximal fitness evaluations (FEs). However, since sequential EAs usually requires a considerable computing time to achieve extensive FEs, we propose a scalable parallel evolutionary optimisation framework that can efficiently deploy parallel EAs over many CPU cores at CPU-only HPC. On the other hand, since EAs using a large number of FEs can produce massive useful information in the course of evolution, we design a surrogate-based approach to learn from this historical information and to better solve complex problems. Then this approach is implemented in parallel based on the proposed scalable parallel framework to achieve remarkable speedups. Since demanding a great computing power on CPU-only HPC is usually very expensive, we design a framework based on GPU-enabled HPC to improve the cost-effectiveness of parallel EAs. The proposed framework can efficiently accelerate parallel EAs using many GPUs and can achieve superior cost-effectiveness. However, since it is very challenging to correctly implement parallel EAs on the GPU, we propose a set of guidelines to verify the correctness of GPU-based EAs. In order to examine these guidelines, they are employed to verify a GPU-based brain storm optimisation that is also proposed in this thesis. In conclusion, the comprehensively experimental study is firstly conducted to investigate the impacts of a large population. After that, a SPEO framework based on CPU-only HPC is proposed and is employed to accelerate a time-consuming implementation of EA. Finally, the correctness verification of implementing EAs based on a single GPU is discussed and the SPEO framework is then extended to be deployed based on GPU-enabled HPC

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Optimisation des colonnes HIDiC, intégrant une mousse métallique, basée sur une étude théorique et expérimentale des transferts thermiques

La distillation est une opération unitaire de séparation qui est largement utilisée. Toutefois, lorsque les volatilités des corps à séparer sont proches, le besoin en énergie de la colonne augmente, et l’efficacité énergétique du procédé de séparation diminue. Ainsi, la faiblesse de la distillation est son efficacité énergétique (au maximum 10 %). La réduction de la consommation énergétique des colonnes à distiller est donc un enjeu majeur dans le contexte énergétique actuel. Une des voies prometteuses est les colonnes à distiller dites HIDiC (Heat Integrated Distillation Column). Dans ce type de configuration, la colonne est scindée en deux colonnes : une colonne d’appauvrissement et une colonne d’enrichissement. La colonne d’appauvrissement opère à un niveau de pression plus faible que la colonne d’enrichissement. Un compresseur ainsi qu’une vanne de détente sont installés pour ajuster les niveaux de pression respectifs dans les deux parties. La différence de pression ainsi établie permet d’imposer une différence de températures qui offre la possibilité de transférer de l’énergie entre les deux colonnes par l’intermédiaire d’une technologie de transfert de chaleur. Dans un premier temps, l’objet de cette étude est de valider une nouvelle technologie de transfert thermique pour les colonnes concentriques HIDiC. Cette technologie innovante, Mousse métallique à cellules ouvertes, est caractérisée et validée en comparant avec un garnissage classique. Pour cela, un pilote expérimental de colonne concentrique contenant le garnissage structuré a été mis en oeuvre au laboratoire. Les résultats des mousses métalliques ont montré une performance thermique plus importante que le garnissage classique avec un gain moyen de 102 %. La conductance thermique des mousses métallique à cellules ouvertes obtenue expérimentalement est de 1285 W.K-1. Ces résultats confirment l’intérêt de l’utilisation du garnissage innovant dans les colonnes de distillation HIDiC en tant que technologie de transfert de chaleur. Dans un deuxième temps, un outil de simulation des colonnes HIDiC est développé dans le logiciel commercial ProSim Plus™®. Par rapport aux colonnes de distillation conventionnelles, les colonnes HIDiC possèdent des paramètres spécifiques tels que le rapport de pression et le profil d’échange de chaleur entre les deux sections de la colonne. Une procédure d’optimisation est élaborée afin d’obtenir une colonne HIDiC avec un coût total annuel « TAC » minimal et une distribution énergétique optimale. La méthode stochastique est adoptée avec un algorithme génétique « AG » ou l’initialisation des variables d’action n’est pas nécessaire. Deux études de cas sont effectuées. L’une est un système largement étudié dans la littérature, le mélange (Benzène/Toluène). La procédure de conception et d’optimisation est évaluée. Une réduction du TAC de 7,4 % et 13,9 % est obtenue par rapport aux précédents travaux de la littérature. L’autre étude de cas est un mélange binaire (Cyclohexane/n-Heptane). Les résultats de la simulation concernant les quantités d’énergie échangées de la colonne d’enrichissement vers la colonne d’appauvrissement sont validés en vérifiant la faisabilité du transfert thermique par la conductance thermique de la technologie innovante obtenue expérimentalement UA (W.K-1)

Innovative hybrid MOEA/AD variants for solving multi-objective combinatorial optimization problems

Orientador : Aurora Trinidad Ramirez PozoCoorientador : Roberto SantanaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 16/12/2016Inclui referências : f. 103-116Resumo: Muitos problemas do mundo real podem ser representados como um problema de otimização combinatória. Muitas vezes, estes problemas são caracterizados pelo grande número de variáveis e pela presença de múltiplos objetivos a serem otimizados ao mesmo tempo. Muitas vezes estes problemas são difíceis de serem resolvidos de forma ótima. Suas resoluções tem sido considerada um desafio nas últimas décadas. Os algoritimos metaheurísticos visam encontrar uma aproximação aceitável do ótimo em um tempo computacional razoável. Os algoritmos metaheurísticos continuam sendo um foco de pesquisa científica, recebendo uma atenção crescente pela comunidade. Uma das têndencias neste cenário é a arbordagem híbrida, na qual diferentes métodos e conceitos são combinados objetivando propor metaheurísticas mais eficientes. Nesta tese, nós propomos algoritmos metaheurísticos híbridos para a solução de problemas combinatoriais multiobjetivo. Os principais ingredientes das nossas propostas são: (i) o algoritmo evolutivo multiobjetivo baseado em decomposição (MOEA/D framework), (ii) a otimização por colônias de formigas e (iii) e os algoritmos de estimação de distribuição. Em nossos frameworks, além dos operadores genéticos tradicionais, podemos instanciar diferentes modelos como mecanismo de reprodução dos algoritmos. Além disso, nós introduzimos alguns componentes nos frameworks objetivando balancear a convergência e a diversidade durante a busca. Nossos esforços foram direcionados para a resolução de problemas considerados difíceis na literatura. São eles: a programação quadrática binária sem restrições multiobjetivo, o problema de programação flow-shop permutacional multiobjetivo, e também os problemas caracterizados como deceptivos. Por meio de estudos experimentais, mostramos que as abordagens propostas são capazes de superar os resultados do estado-da-arte em grande parte dos casos considerados. Mostramos que as diretrizes do MOEA/D hibridizadas com outras metaheurísticas é uma estratégia promissora para a solução de problemas combinatoriais multiobjetivo. Palavras-chave: metaheuristicas, otimização multiobjetivo, problemas combinatoriais, MOEA/D, otimização por colônia de formigas, algoritmos de estimação de distribuição, programação quadrática binária sem restrições multiobjetivo, problema de programação flow-shop permutacional multiobjetivo, abordagens híbridas.Abstract: Several real-world problems can be stated as a combinatorial optimization problem. Very often, they are characterized by the large number of variables and the presence of multiple conflicting objectives to be optimized at the same time. These kind of problems are, usually, hard to be solved optimally, and their solutions have been considered a challenge for a long time. Metaheuristic algorithms aim at finding an acceptable approximation to the optimal solution in a reasonable computational time. The research on metaheuristics remains an attractive area and receives growing attention. One of the trends in this scenario are the hybrid approaches, in which different methods and concepts are combined aiming to propose more efficient approaches. In this thesis, we have proposed hybrid metaheuristic algorithms for solving multi-objective combinatorial optimization problems. Our proposals are based on (i) the multi-objective evolutionary algorithm based on decomposition (MOEA/D framework), (ii) the bio-inspired metaheuristic ant colony optimization, and (iii) the probabilistic models from the estimation of distribution algorithms. Our algorithms are considered MOEA/D variants. In our MOEA/D variants, besides the traditional genetic operators, we can instantiate different models as the variation step (reproduction). Moreover, we include some design modifications into the frameworks to control the convergence and the diversity during their search (evolution). We have addressed some important problems from the literature, e.g., the multi-objective unconstrained binary quadratic programming, the multiobjective permutation flowshop scheduling problem, and the problems characterized by deception. As a result, we show that our proposed frameworks are able to solve these problems efficiently by outperforming the state-of-the-art approaches in most of the cases considered. We show that the MOEA/D guidelines hybridized to other metaheuristic components and concepts is a powerful strategy for solving multi-objective combinatorial optimization problems. Keywords: meta-heuristics, multi-objective optimization, combinatorial problems, MOEA/D, ant colony optimization, estimation of distribution algorithms, unconstrained binary quadratic programming, permutation flowshop scheduling problem, hybrid approaches