A decomposition-based evolutionary algorithm using an estimation strategy for multimodal multi-objective optimization

Abstract

Multimodal multi-objective optimization problems (MMOPs) are commonly seen in real-world applications and have attracted a growing attention in recent years. In this paper, a decomposition-based evolutionary algorithm using an estimation strategy is presented to handle MMOPs. In the proposed algorithm, multiple individuals who are assigned to the same weight vector form a subpopulation. Then, the estimation strategy is designed for estimating the amount of Pareto optimal sets (PSs), where the mean-shift algorithm is adopted to classify subpopulation into some clusters. In essence, the number of clusters is considered to be the estimated number of PSs. Finally, an environmental selection method, which combines the estimation strategy and the greedy selection, is adopted to dynamically adjust the subpopulation scale for maintaining the population diversity. The experimental results illustrate that the devised algorithm performs pass beyond the selected up-to-date competing algorithms.