A Competition-Cooperation Evolutionary Algorithm with Bidirectional Multi-population Local Search and Local Hypervolume-based Strategy for Multi-objective Optimization

摘要

This paper proposes a competition-cooperation algorithm with bidirectional multi-population local search and local hypervolume-based strategy (CCLS) to solve multi-objective optimization problems. In the proposed method, a bidirectional multi-population local search is devised and used to speed up convergence while keeping rich population diversity. It searches along two completely opposite directions, and adopts a unique strategy to perform replacement operation. Furthermore, a local hypervolume-based strategy has been designed. It combines Pareto dominance relation with hypervolume indicator to maintain population diversity. The proposed algorithm is applied to the widely used bi-objective and tri-objective test problems, and compared with related methods. The results demonstrate that the proposed algorithm generally outperforms related methods.