关键词: 超多目标优化, 进化算法, 子区域划分, 自适应加权, 密度估计

Abstract: To address the challenge of balancing convergence and diversity in multi-objective evolutionary algorithms when solving many-objective optimization problems (MaOPs), a subregion-based many-objective evolutionary algorithm (SR-MaOEA) is proposed. In this approach, the structure of the objective space is constructed by using a subregion division strategy, and the subregion density is quantified via shift-based density estimation (SDE). A hierarchical subregion dominance relation is then established to enhance the selection pressure among individuals. Furthermore, a convergence-diversity adaptive weighted selection mechanism is devised, in which the potential of each subregion is estimated by dynamically calculating the difference in weighted sums across successive generations. Individuals in subregions with higher potential are preferentially retained to update the population. Comparative experiments on the MaF benchmark suite are conducted against several representative many-objective optimization algorithms. The results demonstrate that SR-MaOEA exhibits competitive performance and robustness in high-dimensional objective spaces.

Key words: many-objective evolutionary, evolutionary algorithms, subregion partitioning, adaptive weighting, density estimation