An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA

Decomposition-based evolutionary multiobjective algorithms (MOEAs) divide a multiobjective problem 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 enco...

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Autores principales:	Wenbo Qiu, Jianghan Zhu, Huangchao Yu, Mingfeng Fan, Lisu Huo
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/703999e7051944b0964f69800388dc27
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spelling	oai:doaj.org-article:703999e7051944b0964f69800388dc272021-11-29T00:57:08ZAn Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA1099-052610.1155/2021/8870356https://doaj.org/article/703999e7051944b0964f69800388dc272021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8870356https://doaj.org/toc/1099-0526Decomposition-based evolutionary multiobjective algorithms (MOEAs) divide a multiobjective problem 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 vector adjustment strategy. By adding the strategy, the predefined reference vectors will be adjusted according to the distribution of promising solutions with good overall performance and the subspaces in which the PF lies may be further divided 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 increase of objective size. Motivated by that, an improved angle-penalized distance (APD) method 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-, and 10-objective MaF1–MaF9. The results demonstrate that the proposed algorithm obtains the best overall performance.Wenbo QiuJianghan ZhuHuangchao YuMingfeng FanLisu HuoHindawi-WileyarticleElectronic computers. Computer scienceQA75.5-76.95ENComplexity, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Wenbo Qiu Jianghan Zhu Huangchao Yu Mingfeng Fan Lisu Huo An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA
description	Decomposition-based evolutionary multiobjective algorithms (MOEAs) divide a multiobjective problem 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 vector adjustment strategy. By adding the strategy, the predefined reference vectors will be adjusted according to the distribution of promising solutions with good overall performance and the subspaces in which the PF lies may be further divided 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 increase of objective size. Motivated by that, an improved angle-penalized distance (APD) method 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-, and 10-objective MaF1–MaF9. The results demonstrate that the proposed algorithm obtains the best overall performance.
format	article
author	Wenbo Qiu Jianghan Zhu Huangchao Yu Mingfeng Fan Lisu Huo
author_facet	Wenbo Qiu Jianghan Zhu Huangchao Yu Mingfeng Fan Lisu Huo
author_sort	Wenbo Qiu
title	An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA
title_short	An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA
title_full	An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA
title_fullStr	An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA
title_full_unstemmed	An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA
title_sort	adaptive reference vector adjustment strategy and improved angle-penalized value method for rvea
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/703999e7051944b0964f69800388dc27
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An Adaptive Reference Vector Adjustment Strategy and Improved Angle-Penalized Value Method for RVEA

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