Population Initialization for Evolutionary Multi-objective Optimization: A Short Review

Cheng Gong, Ping Guo, Lie Meng Pang, Qingfu Zhang, Hisao Ishibuchi

Abstract: As the initial phase of evolutionary multi-objective optimization (EMO), population initialization is always essential for subsequent evolutionary processes aimed at solving multi-objective optimization problems. While random initialization (i.e., random sampling) remains the most frequently used initialization method in EMO algorithms, many studies have indicated that the utilization of alternative initialization methods instead of random initialization can significantly improve the performance of the original EMO algorithms. Some studies have also investigated the effects of different initialization techniques or parameters (e.g., methods and population size). However, there is a scarcity of recent reviews focusing on population initialization for EMO algorithms. To bridge this research gap in the EMO research community, this paper provides a short review on this crucial topic. Specifically, the current choice of initialization methods is briefly summarized by using some representative EMO algorithms. The effects of population initialization and new initialization method design are then extensively reviewed. Insights into population initialization are also provided. This study aims to provide a comprehensive understanding of population initialization for newcomers to the EMO community.