A decomposition-based hybrid evolutionary algorithm for multi-modal multi-objective optimization

Y. Peng; H. Ishibuchi

10.1109/SMC52423.2021.9659132

2021

Proc. of 2021 IEEE International Conference on Systems, Man, and Cybernetics

1062-922X

978-1-6654-4208-4

2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)

160-167

October 17-20, 2021

Melbourne, Australia

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Multi-modal multi-objective optimization problems (MMOPs) have received increasing attention from the evolutionary multi-objective optimization community. To solve MMOPs, an optimizer is required to locate multiple sets of Pareto optimal solutions in the decision space. In this paper, a novel decomposition-based hybrid evolutionary algorithm is proposed for handling MMOPs efficiently. In the proposed algorithm, each reference vector is associated with a sub-population. In this manner, each reference vector is able to preserve multiple optima of the corresponding sub-problem in its own sub-population. In each generation, the following three procedures are used to update each sub-population. First, the sub-population evolves independently based on the deterministic crowding mechanism to maintain the diversity in the decision space. Second, the sub-population evolves in a collaborative manner with neighboring sub-populations. Subsequently, solutions that are converging to the same optimal solution are identified. All identified solutions except for the best one are re-initialized. This mechanism impels the solutions in each sub-population to converge to different optima in the decision space. Experimental results show that the proposed algorithm achieves superior performance in comparison with four state-of-the-art algorithms on various test problems.

Multi-objective optimization multi-modal multi-objective optimization niching MOEA/D

第一

英语

CPCI-S ; EI

National Natural Science Foundation of China[61876075]

Computer Science

Computer Science, Cybernetics ; Computer Science, Information Systems

WOS:000800532000022

20220711617156

Evolutionary algorithms ; Optimal systems ; Pareto principle

Optimization Techniques:921.5 ; Systems Science:961

人工提交

Southern University of Science and Technology

Y. Peng,H. Ishibuchi. A decomposition-based hybrid evolutionary algorithm for multi-modal multi-objective optimization[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:160-167.