An Adaptive Metaheuristic for Unconstrained Multimodal Numerical Optimization

Borges, Hélder Pereira; Cortes, Omar Andrés Carmona; Vieira, Dário

doi:10.1007/978-3-319-91641-5_3

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…No caso de funções não separáveis, existe uma dependência entre as variáveis que não permite a separação, fazendo com que sua otimização seja ainda mais desafiadora. De qualquer forma essas funções são comuns, sendo encontradas em trabalho, como por exemplo, [8], [9] e [10], dentre outros.…”

Section: Introductionunclassified

Um Estudo Sobre Meta-Heurísticas em Otimização Multimodal

Cortes¹,

Silva²

2020

Anais Do XI Computer on the Beach - COTB '20

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RESUMOThis paper presents an investigation of four metaheuristics for multimodaloptimization. The algorithms have been implemented inR and compared against each other using well-known benchmarkfunctions. We implemented the following algorithms: Genetic Algorithm(GA), Particle Swarm Optimization (PSO), Differential Evolution(DE), and Bat Algorithm (BA). For comparisons, we used fivemultimodal benchmarks: Rosenbrock, Griewank, Ackley, Schwefel,and Alpine. Preliminary results show that Bat Algorithm and GeneticAlgorithm tend to discover the best solution considering theparameters that have been set up.

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Section: Introductionunclassified

Um Estudo Sobre Meta-Heurísticas em Otimização Multimodal

Cortes¹,

Silva²

2020

Anais Do XI Computer on the Beach - COTB '20

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“…A set of realworld problems can be seen in Averick et al (1992). Those functions have been used to test di erent kind of algorithms as we can see in Borges et al (2018), Maucec and Brest (2018), Karaboğa and Kaya (2018), Cavalca and Fernandes (2018), etc.…”

Section: Introductionmentioning

confidence: 99%

Unconstrained numerical optimization using real-coded genetic algorithms: a study case using benchmark functions in R from Scratch

Cortes¹,

Silva²

2019

RBCA

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Unconstrained numerical problems are common in solving practical applications that, due to its nature, are usually devised by several design variables, narrowing the kind of technique or algorithm that can deal with them. An interesting way of tackling this kind of issue is to use an evolutionary algorithm named Genetic Algorithm. In this context, this work is a tutorial on using real-coded genetic algorithms for solving unconstrained numerical optimization problems. We present the theory and the implementation in R language. Five benchmarks functions (Rosenbrock, Griewank, Ackley, Schwefel, and Alpine) are used as a study case. Further, four different crossover operators (simple, arithmetical, non-uniform arithmetical, and Linear), two selection mechanisms (roulette wheel and tournament), and two mutation operators (uniform and non-uniform) are shown. Results indicate that non-uniform mutation and tournament selection tend to present better outcomes.

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“…Já em Borges et. al [1] recomenda-se estocasticamente qual meta-heurística utilizar dentre GA, PSO e DE em problemas com objetivoúnico. Já em aplicações multiobjetivo, Costa [4] escolhe estocasticamente entre GA, PSO e DE para fazer a atualização da população no SPEA2 em busca de novas soluções não dominadas.…”

Section: Introductionunclassified

Uma Nova Meta-heurística Adaptativa Baseada em Vetor de Avaliações para Otimização de Portfólios de Investimentos

Cortes¹,

Costa

2019

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This article describes a new adaptive metaheuristic based on a vector evaluated approach for solving multiobjective problems. We called our proposed algorithm Vector Evaluated Meta-Heuristic. Its main idea is to evolve two populations independently, exchanging information between them, i.e., the first population evolves according to the best individual of the second population and vice-versa. The choice of which algorithm will be executed on each generation is carried out stochastically among three evolutionary algorithms well known in the literature: PSO, DE, ABC. In order to evaluate the results, we used an established metric in multiobjective evolutionary algorithms called hypervolume. Tests have shown that the adaptive metaheuristic reaches the best hyper-volumes in three of ZDT benchmarks functions and, also, in two portfolios of a real-world problem called portfolio investment optimization. The results show that our algorithm improved the Pareto curve when compared to the hypervolumes of each heuristic separately.

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An Adaptive Metaheuristic for Unconstrained Multimodal Numerical Optimization

Cited by 5 publications

References 10 publications

Um Estudo Sobre Meta-Heurísticas em Otimização Multimodal

Um Estudo Sobre Meta-Heurísticas em Otimização Multimodal

Unconstrained numerical optimization using real-coded genetic algorithms: a study case using benchmark functions in R from Scratch

Uma Nova Meta-heurística Adaptativa Baseada em Vetor de Avaliações para Otimização de Portfólios de Investimentos

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