A Multi-Population Multi-Objective Evolutionary Algorithm Based on the Contribution of Decision Variables to Objectives for Large-Scale Multi/Many-Objective Optimization

Most existing multiobjective evolutionary algorithms treat all decision variables as a whole to perform genetic operations and optimize all objectives with one population at the same time. Considering different control attributes, different decision variables have different optimization effects on e...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 53; no. 11; pp. 6998 - 7007
Main Authors: Xu, Ying, Xu, Chong, Zhang, Huan, Huang, Lei, Liu, Yiping, Nojima, Yusuke, Zeng, Xiangxiang
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Clustering algorithms
Convergence
Decision variables analysis
Diversity methods
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
large-scale manyobjective optimization
large-scale multiobjective optimization
Mathematical analysis
multiobjective evolutionary algorithm
Multiple objective analysis
multipopulation multiobjective evolutionary algorithm
Optimization
Sociology
Statistics
Variables
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:Most existing multiobjective evolutionary algorithms treat all decision variables as a whole to perform genetic operations and optimize all objectives with one population at the same time. Considering different control attributes, different decision variables have different optimization effects on each objective, so decision variables can be divided into convergence- or diversity-related variables. In this article, we propose a new metric called the optimization degree of the convergence-related decision variable to each objective to calculate the contribution objective of each decision variable. All decision variables are grouped according to their contribution objectives. Then, a multiobjective evolutionary algorithm, namely, decision variable contributing to objectives evolutionary algorithm (DVCOEA), has been proposed. In order to balance the convergence and diversity of the population, the DVCOEA algorithm combines the multipopulation multiobjective framework, where two different optimization strategies are designed to optimize the subpopulation and individuals in the external archive, respectively. Finally, DVCOEA is compared with several state-of-the-art algorithms on a number of benchmark functions. Experimental results show that DVCOEA is a competitive approach for solving large-scale multi/many-objective problems.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3180214