A Decomposition-based Hybrid Evolutionary Algorithm for Multi-modal Multi-objective Optimization

Peng，Yiming; Ishibuchi，Hisao

10.1109/SMC52423.2021.9659132

2021

1062-922X

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

160-167

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.

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

第一 ; 通讯

英语

EI

National Natural Science Foundation of China[61876075];

20220711617156

Evolutionary algorithms ; Optimal systems ; Pareto principle

Optimization Techniques:921.5 ; Systems Science:961

2-s2.0-85124285916

Scopus

Southern University of Science and Technology,Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,518055,China

Peng，Yiming,Ishibuchi，Hisao. A Decomposition-based Hybrid Evolutionary Algorithm for Multi-modal Multi-objective Optimization[C],2021:160-167.