Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization

The decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX)...

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Autores principales:	Yifei Sun, Kun Bian, Zhuo Liu, Xin Sun, Ruoxia Yao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/f14ad4450c044c63a132e65bd86bfae7
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spelling	oai:doaj.org-article:f14ad4450c044c63a132e65bd86bfae72021-11-29T00:56:44ZAdaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization1607-887X10.1155/2021/2491796https://doaj.org/article/f14ad4450c044c63a132e65bd86bfae72021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2491796https://doaj.org/toc/1607-887XThe decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator with differential evolution (DE) operator, which is used to enhance the diversity of the solutions more effectively. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such a prematurity problem, this paper proposes three different adaptive operators in DE with crossover probability and amplification factors to adjust the parameter settings adaptively. We incorporate these three adaptive operators in MOEA/D-DE and MOEA/D-PaS to solve MOPs and many-objective optimization problems (MaOPs), respectively. This paper also designs a sensitive experiment for the changeable parameter η in the proposed adaptive operators to explore how η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems, including ZDT, DTLZ, WFG, and MaF test suites. The experimental results illustrate that the three proposed adaptive algorithms have better performance on most benchmark problems.Yifei SunKun BianZhuo LiuXin SunRuoxia YaoHindawi LimitedarticleMathematicsQA1-939ENDiscrete Dynamics in Nature and Society, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematics QA1-939
spellingShingle	Mathematics QA1-939 Yifei Sun Kun Bian Zhuo Liu Xin Sun Ruoxia Yao Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization
description	The decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator with differential evolution (DE) operator, which is used to enhance the diversity of the solutions more effectively. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such a prematurity problem, this paper proposes three different adaptive operators in DE with crossover probability and amplification factors to adjust the parameter settings adaptively. We incorporate these three adaptive operators in MOEA/D-DE and MOEA/D-PaS to solve MOPs and many-objective optimization problems (MaOPs), respectively. This paper also designs a sensitive experiment for the changeable parameter η in the proposed adaptive operators to explore how η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems, including ZDT, DTLZ, WFG, and MaF test suites. The experimental results illustrate that the three proposed adaptive algorithms have better performance on most benchmark problems.
format	article
author	Yifei Sun Kun Bian Zhuo Liu Xin Sun Ruoxia Yao
author_facet	Yifei Sun Kun Bian Zhuo Liu Xin Sun Ruoxia Yao
author_sort	Yifei Sun
title	Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization
title_short	Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization
title_full	Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization
title_fullStr	Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization
title_full_unstemmed	Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization
title_sort	adaptive strategies based on differential evolutionary algorithm for many-objective optimization
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/f14ad4450c044c63a132e65bd86bfae7
work_keys_str_mv	AT yifeisun adaptivestrategiesbasedondifferentialevolutionaryalgorithmformanyobjectiveoptimization AT kunbian adaptivestrategiesbasedondifferentialevolutionaryalgorithmformanyobjectiveoptimization AT zhuoliu adaptivestrategiesbasedondifferentialevolutionaryalgorithmformanyobjectiveoptimization AT xinsun adaptivestrategiesbasedondifferentialevolutionaryalgorithmformanyobjectiveoptimization AT ruoxiayao adaptivestrategiesbasedondifferentialevolutionaryalgorithmformanyobjectiveoptimization
_version_	1718407684766564352

Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization

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