MPCMO: An improved multi-population co-evolutionary algorithm for many-objective optimization

Abstract

Many-objective optimization problems (MaOPs) are widely used in scientific research and engineering practices, which mainly consider joint optimization of multiple objectives simultaneously. Despite the numerous multi-objective evolutionary algorithms proposed in recent years, they often struggle with challenges in fitness assignment arising from objective conflicts. Meanwhile, they tend to perform well in only one aspect of convergence, diversity, and computational complexity. To address these issues, this paper proposes an improved multi-population co-evolutionary algorithm for many-objective optimization (termed MPCMO), which leverages the advantages of multi-population co-evolutionary techniques. The primary objective of MPCMO is to achieve a more balanced performance across convergence, diversity, and complexity. MPCMO comprises three essential components. Initially, an adaptive evolutionary strategy is employed to dynamically allocate evolutionary opportunities to subpopulations so as to conserve computational resources and enhance convergence. Subsequently, a migration strategy is developed to ensure a more global approximation of whole Pareto front. Additionally, an archive update-truncation strategy, based on angle selection and shift-based density estimation, is adopted to enhance diversity. We conduct comprehensive comparative experiments on a variety of many-objective benchmark problems with complicated characteristics. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art algorithms in terms of both diversity and convergence.