Parameter-Adaptive Paired Offspring Generation for Constrained Large-Scale Multiobjective Optimization Algorithm

Haiyue Zhu, Qingda Chen, Jinliang Ding, Xingyi Zhang, Hongfeng Wang

Abstract: There are significant challenges in designing optimization algorithms for constrained large-scale multiobjective optimization problems due to numerous decision variables and constraints. For example, the decision space size exponentially grows with the number of decision variables, and constraints restrict the feasible range, increasing the complexity of the search space. To solve these problems, this paper presents a constrained large-scale multiobjective optimization algorithm based on adaptive paired offspring generation (aPOCEA). Specifically, an adaptive parameter adjustment strategy is proposed to determine the number of solutions in each subpopulation and balance the exploration and exploitation ability of the algorithm, enhancing the convergence speed of aPOCEA. Meanwhile, we propose a parent selection strategy to select high-quality parent solutions, increasing the probability of generating high-quality offspring solutions. Experimental results on ten benchmarks, each with two to three objectives, multiple constraints, and hundreds of decision variables, demonstrate that aPOCEA outperforms other representative optimization algorithms.

External IDs:dblp:conf/ssci/ZhuCDZW23