A Many-Objective Evolutionary Algorithm Based on Indicator and Decomposition

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 mul...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 11; no. 2; p. 413
Main Authors: Xia, Yizhang, Huang, Jianzun, Li, Xijun, Liu, Yuan, Zheng, Jinhua, Zou, Juan
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2023
Subjects:
Algorithms
Convergence
Decomposition
evolutionary algorithm
Evolutionary algorithms
learning population
many-objective optimization
Methods
Multiple objective analysis
Optimization
reference point adjustment
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math11020413