Solving dynamic multi-objective problems with a new prediction-based optimization algorithm.

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-...

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Autores principales:	Qingyang Zhang, Shouyong Jiang, Shengxiang Yang, Hui Song
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/46b166da3d704e36930035a5d6b90d85
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spelling	oai:doaj.org-article:46b166da3d704e36930035a5d6b90d852021-12-02T20:18:49ZSolving dynamic multi-objective problems with a new prediction-based optimization algorithm.1932-620310.1371/journal.pone.0254839https://doaj.org/article/46b166da3d704e36930035a5d6b90d852021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254839https://doaj.org/toc/1932-6203This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.Qingyang ZhangShouyong JiangShengxiang YangHui SongPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0254839 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qingyang Zhang Shouyong Jiang Shengxiang Yang Hui Song Solving dynamic multi-objective problems with a new prediction-based optimization algorithm.
description	This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.
format	article
author	Qingyang Zhang Shouyong Jiang Shengxiang Yang Hui Song
author_facet	Qingyang Zhang Shouyong Jiang Shengxiang Yang Hui Song
author_sort	Qingyang Zhang
title	Solving dynamic multi-objective problems with a new prediction-based optimization algorithm.
title_short	Solving dynamic multi-objective problems with a new prediction-based optimization algorithm.
title_full	Solving dynamic multi-objective problems with a new prediction-based optimization algorithm.
title_fullStr	Solving dynamic multi-objective problems with a new prediction-based optimization algorithm.
title_full_unstemmed	Solving dynamic multi-objective problems with a new prediction-based optimization algorithm.
title_sort	solving dynamic multi-objective problems with a new prediction-based optimization algorithm.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/46b166da3d704e36930035a5d6b90d85
work_keys_str_mv	AT qingyangzhang solvingdynamicmultiobjectiveproblemswithanewpredictionbasedoptimizationalgorithm AT shouyongjiang solvingdynamicmultiobjectiveproblemswithanewpredictionbasedoptimizationalgorithm AT shengxiangyang solvingdynamicmultiobjectiveproblemswithanewpredictionbasedoptimizationalgorithm AT huisong solvingdynamicmultiobjectiveproblemswithanewpredictionbasedoptimizationalgorithm
_version_	1718374221177946112

Solving dynamic multi-objective problems with a new prediction-based optimization algorithm.

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