Solving Multimodal Multi-Objective Problems with Local Pareto Front using a Population Clustering Mechanism

Abstract

Most existing multimodal multi-objective evolutionary algorithms only search the global Pareto front of the problem while ignoring the excellent local Pareto front of the problem. To address this issue, an optimization algorithm with population clustering mechanism is proposed to settle multimodal multi-objective problems with local Pareto front. At the first step, a partitioning method is used to divide the total population into main rank and other ranks and a population clustering method is proposed to repartition the entire population into global Pareto front subpopulations and local Pareto front subpopulations. In the second step, each subpopulation evolves independently and the diversity in the objective space and decision space are considered simultaneously. An improved density adaptive adjustment strategy is put forward to enhance the diversity of the population in the decision space. In the experimental part, the algorithm is compared with several other state-of-the-art algorithms using the CEC 2019 MMOPs test case, and the result of the experiment confirm that the algorithm proposed shows excellent performance.