A New Decomposition-Based Many-Objective Algorithm Based on Adaptive Reference Vectors and Fractional Dominance Relation

Decomposition-based evolutionary multi-objective algorithms (MOEAs) and many-objective algorithms (MaOEAs) divide a multi-objective problem (MOP) or a many-objective problem (MaOP) into several subproblems by using a set of predefined uniformly distributed reference vectors and can achieve good over...

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Autor principal:	Xiaojun Zhang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Reference vector decomposition-based MaOEA adaptive reference-vector adjustment strategy Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/7b8d933d6f0f4b2ab88488316d0e874e
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id	oai:doaj.org-article:7b8d933d6f0f4b2ab88488316d0e874e
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spelling	oai:doaj.org-article:7b8d933d6f0f4b2ab88488316d0e874e2021-11-20T00:00:51ZA New Decomposition-Based Many-Objective Algorithm Based on Adaptive Reference Vectors and Fractional Dominance Relation2169-353610.1109/ACCESS.2021.3126292https://doaj.org/article/7b8d933d6f0f4b2ab88488316d0e874e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606712/https://doaj.org/toc/2169-3536Decomposition-based evolutionary multi-objective algorithms (MOEAs) and many-objective algorithms (MaOEAs) divide a multi-objective problem (MOP) or a many-objective problem (MaOP) into several subproblems by using a set of predefined uniformly distributed reference vectors and can achieve good overall performance especially in maintaining population diversity. However, they encounter huge difficulties in addressing problems with irregular Pareto Fronts (PFs) since many reference vectors do not work during the searching process. To cope with this problem, this paper aims to improve an existing decomposition-based algorithm called reference vector guided evolutionary algorithm (RVEA) by designing an adaptive reference vectors adjustment strategy and strengthening the poor selection pressure. By adding the adaptive strategy, the predefined reference vectors will be dynamically adjusted according to the distribution of promising solutions with good overall performance and the subspaces where the PF lies may be further divided so as to contribute more to the searching process. Besides, the selection pressure with respect to convergence performance posed by RVEA is mainly from the length of normalized objective vectors and the metric is poor in evaluating the convergence performance of a solution with the increasing of objective size. Motivated by that, an improved angle-penalized distance (APD) method based on a newly proposed fractional dominance relation is developed to better distinguish solutions with sound convergence performance in each subspace. To investigate the performance of the proposed algorithm, extensive experiments are conducted to compare it with 5 state-of-the-art decomposition-based algorithms on 3-, 5-, 8-, 10- objective MaF1-MaF9. The results demonstrate that the proposed algorithm obtains the best overall performance.Xiaojun ZhangIEEEarticleReference vectordecomposition-based MaOEAadaptive reference-vector adjustment strategyElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152169-152181 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Reference vector decomposition-based MaOEA adaptive reference-vector adjustment strategy Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Reference vector decomposition-based MaOEA adaptive reference-vector adjustment strategy Electrical engineering. Electronics. Nuclear engineering TK1-9971 Xiaojun Zhang A New Decomposition-Based Many-Objective Algorithm Based on Adaptive Reference Vectors and Fractional Dominance Relation
description	Decomposition-based evolutionary multi-objective algorithms (MOEAs) and many-objective algorithms (MaOEAs) divide a multi-objective problem (MOP) or a many-objective problem (MaOP) into several subproblems by using a set of predefined uniformly distributed reference vectors and can achieve good overall performance especially in maintaining population diversity. However, they encounter huge difficulties in addressing problems with irregular Pareto Fronts (PFs) since many reference vectors do not work during the searching process. To cope with this problem, this paper aims to improve an existing decomposition-based algorithm called reference vector guided evolutionary algorithm (RVEA) by designing an adaptive reference vectors adjustment strategy and strengthening the poor selection pressure. By adding the adaptive strategy, the predefined reference vectors will be dynamically adjusted according to the distribution of promising solutions with good overall performance and the subspaces where the PF lies may be further divided so as to contribute more to the searching process. Besides, the selection pressure with respect to convergence performance posed by RVEA is mainly from the length of normalized objective vectors and the metric is poor in evaluating the convergence performance of a solution with the increasing of objective size. Motivated by that, an improved angle-penalized distance (APD) method based on a newly proposed fractional dominance relation is developed to better distinguish solutions with sound convergence performance in each subspace. To investigate the performance of the proposed algorithm, extensive experiments are conducted to compare it with 5 state-of-the-art decomposition-based algorithms on 3-, 5-, 8-, 10- objective MaF1-MaF9. The results demonstrate that the proposed algorithm obtains the best overall performance.
format	article
author	Xiaojun Zhang
author_facet	Xiaojun Zhang
author_sort	Xiaojun Zhang
title	A New Decomposition-Based Many-Objective Algorithm Based on Adaptive Reference Vectors and Fractional Dominance Relation
title_short	A New Decomposition-Based Many-Objective Algorithm Based on Adaptive Reference Vectors and Fractional Dominance Relation
title_full	A New Decomposition-Based Many-Objective Algorithm Based on Adaptive Reference Vectors and Fractional Dominance Relation
title_fullStr	A New Decomposition-Based Many-Objective Algorithm Based on Adaptive Reference Vectors and Fractional Dominance Relation
title_full_unstemmed	A New Decomposition-Based Many-Objective Algorithm Based on Adaptive Reference Vectors and Fractional Dominance Relation
title_sort	new decomposition-based many-objective algorithm based on adaptive reference vectors and fractional dominance relation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/7b8d933d6f0f4b2ab88488316d0e874e
work_keys_str_mv	AT xiaojunzhang anewdecompositionbasedmanyobjectivealgorithmbasedonadaptivereferencevectorsandfractionaldominancerelation AT xiaojunzhang newdecompositionbasedmanyobjectivealgorithmbasedonadaptivereferencevectorsandfractionaldominancerelation
_version_	1718419841900085248

A New Decomposition-Based Many-Objective Algorithm Based on Adaptive Reference Vectors and Fractional Dominance Relation

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