A multi-modal multi-objective algorithm based on diversity indicator ranking

Abstract

Abstract: In multi-modal multi-objective optimization, the diversity in both the decision space and the objective space plays a critical role in the impact on the performance of the algorithm. To better balance the diversity between the two, proposes a multi-modal multi-objective optimization algorithm based on diversity index ranking (D-DNEAL) is proposed in this paper. First, D-DNEAL constructs a diversity indicator using the K-nearest neighbor density estimation method, which is then integrated into the fast non-dominated sorting process to rank individuals. This ensures that individuals with poor convergence but good distribution still have a chance to be selected for the next generation, thereby improving the search ability of the population. Additionally, multi-frontier archiving mechanism is introduced to preserve the individuals with better diversity, so that the algorithm can obtain more local optimal solutions. To verify the performance of D-DNEAL on multi-modal multi-objective optimization problems, six state-of-the-art algorithms are used to make a comparison on 28 multi-modal multi-objective optimization test problems. The results show that the D-DNEAL is effective in solving multi-modal multi-objective problems.

Key words: multi-modal multi-objective optimization, diversity indicator, multiple frontier archives, double niche fitness sharing function, non-dominant sort

CLC Number:

TP18

CAO Jie, QI Zhi, CHEN Zuo-han, ZHANG Jian-lin. A multi-modal multi-objective algorithm based on diversity indicator ranking[J]. Journal of Lanzhou University of Technology, 2025, 51(5): 81-91.

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