A Many-Objective Evolutionary Algorithm Based on Indicator and Decomposition

Yizhang Xia, Jianzun Huang, Xijun Li, Yuan Liu, Jinhua Zheng and Juan Zou ()
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Yizhang Xia: School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China
Jianzun Huang: School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China
Xijun Li: School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China
Yuan Liu: School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China
Jinhua Zheng: School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China
Juan Zou: School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China

Mathematics, 2023, vol. 11, issue 2, 1-27

Abstract: In the field of many-objective evolutionary optimization algorithms (MaOEAs), how to maintain the balance between convergence and diversity has been a significant research problem. With the increase of the number of objectives, the number of mutually nondominated solutions increases rapidly, and multi-objective evolutionary optimization algorithms, based on Pareto-dominated relations, become invalid because of the loss of selection pressure in environmental selection. In order to solve this problem, indicator-based many-objective evolutionary algorithms have been proposed; however, they are not good enough at maintaining diversity. Decomposition-based methods have achieved promising performance in keeping diversity. In this paper, we propose a MaOEA based on indicator and decomposition (IDEA) to keep the convergence and diversity simultaneously. Moreover, decomposition-based algorithms do not work well on irregular PFs. To tackle this problem, this paper develops a reference-points adjustment method based on the learning population. Experimental studies of several well-known benchmark problems show that IDEA is very effective compared to ten state-of-the-art many-objective algorithms.

Keywords: evolutionary algorithm; many-objective optimization; reference point adjustment; learning population (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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