A Subregion Division-Based Evolutionary Algorithm with Effective Mating Selection for Many-Objective Optimization

A variety of evolutionary algorithms have been proposed for many-objective optimization in recent years. However, the difficulties in balancing the convergence and diversity of the population and selecting promising parents for offspring reproduction remain. In this paper, we propose a subregion division-based evolutionary algorithm with an effective mating selection strategy, termed SdEA, for many-objective optimization. In SdEA, a subregion division approach is proposed to divide the objective space into different subregions for balancing the diversity and convergence of the population. Besides, an effective mating selection strategy is proposed to enhance the diversity of the mating pool solutions, aimed at enhancing the selection probability of solutions in the sparse subregions. The proposed SdEA is compared with five state-of-the-art many-objective evolutionary algorithms on 23 test problems from DTLZ, WFG, and MaF test suites. Experimental results on these problems demonstrate that the proposed algorithm is competitive in solving many-objective problems. Furthermore, the proposed mating selection strategy is embedded in several evolutionary algorithms and experimental results demonstrate its effectiveness on improving the performance of the embedded algorithms.