A Multi-modal Multi-objective Optimization Algorithm Based on Adaptive Search

doi:10.12068/j.issn.1005-3026.2023.10.006

Abstract

Abstract: The current decomposition-based multi-modal multi-objective optimization algorithms have insufficient population search capability， useless solutions in sub-populations， and a non-universal distance metric. To address these issues， an adaptive search multi-modal multi-objective optimization algorithm MOEA/D-AS is proposed. Firstly， this method increases the number of reference vectors by reducing the size of the average sub-population. Secondly， the sub-populations are reallocated according to the current state of the sub-populations in the iteration. Finally， a clear distance based on local population information is introduced as the basis for modifying the sub-populations. The proposed algorithm is compared with four algorithms on the 2019 CEC multi-modal multi-objective test problems and the large-scale multi-modal multi-objective test problems for experiments. The experimental results show that the proposed algorithm can effectively solve the multi-modal multi-objective optimization problems.

Key words: multi-modal multi-objective optimization algorithm; adaptive search; sub-population; local information; clear distance

CLC Number:

TP18

LI Zhan-shan， SONG Zhi-yang， HUA Yun-qiao. A Multi-modal Multi-objective Optimization Algorithm Based on Adaptive Search[J]. Journal of Northeastern University(Natural Science), 2023, 44(10): 1408-1415.

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