首页|A Research Mode Based Evolutionary Algorithm for Many-Objective Optimization

A Research Mode Based Evolutionary Algorithm for Many-Objective Optimization

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The development of algorithms to solve Many-objective optimization problems (MaOPs) has attracted significant research interest in recent years. Solving various types of Pareto front (PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm (RMEA) is proposed for many-objective optimization. The archive in the RMEA is used to store non-dominated solutions that can reflect the shape of the PF to guide the reference vector adaptation. Information concerning the population is collected, once the number of non-dominated solutions reaches its limit after many generations without exceeding a given thresh-old, RMEA introduces a research mode that generates more reference vectors to search through the solutions. The proposed algorithm showed competitive performance with four state-of-the-art evolutionary algorithms in a large number of experiments.

Many-objective optimizationReference vectorResearch modeEvolutionary algorithm

CHEN Guoyu、LI Junhua

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Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063, China

This work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaJiangxi Provincial Natural Science FoundationJiangxi Provincial Natural Science Foundation

614400496186602520161BAB20203820181BCB24008

2019

10.1049/cje.2019.05.003

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(4)
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