A multipopulation particle swarm optimization based on divergent guidance and knowledge transfer for multimodal multiobjective problems

Locating and maintaining multiple Pareto optimal sets (PSs) in the decision space simultaneously is a challenging issue in solving multimodal multiobjective optimization problems (MMOPs). To deal with this challenge, this paper proposed a multipopulation particle swarm optimization based on divergent guidance and knowledge transfer (MPPSO-DGKT). First, a divergent guidance strategy is proposed to utilize the information of superior and inferior particles in the subpopulation. This strategy can alleviate the premature convergence due to the excessive influence of the global Pareto optimal solutions found so far. Second, a knowledge transfer strategy is developed to promote the knowledge transfer between different subpopulations, which can enhance the exploitation ability of the population. Finally, the update and selection strategy is used to keep more promising nondominated solutions, which can help the algorithm to obtain global and local PSs. To verify the effectiveness of the proposed algorithm, MPPSO-DGKT is compared with seven state-of-the-art multimodal multiobjective optimization algorithms on CEC2020 competition. Experimental results indicate that the proposed algorithm is more competitive than its competitors when solving MMOPs with both global and local PSs.