Large-Scale Constrained Multiobjective Optimization Based on Variable Adaptive Optimization and Population Reconstruction

Abstract

Constrained multiobjective evolutionary algorithms (CMOEAs) have been proposed to address constrained multiobjective optimization problems (CMOPs), and they have shown promising performance on small-scale problems. However, for CMOPs with large-scale decision variables, complex coupling relationships among variables and the huge search space make the algorithm converge slowly and difficult to find feasible solutions. Therefore, this study proposes a large-scale CMOEA based on variable adaptive optimization and population reconstruction to better solve large-scale CMOPs. First, a variable adaptive optimization strategy is developed in the global search stage, where convergence-related and diversity-related variables are adaptively selected for optimization. This strategy reduces the variable optimization difficulty caused by complex coupling relationships and explores the entire search space to locate regions near the constrained Pareto front (CPF). Then, in the local search stage, an archive-assisted population reconstruction strategy is proposed to generate an initial population by using promising solutions from the global search stage to rapidly guide the population towards the CPF. The experiments are conducted on 43 benchmark problems with up to 1000 decision variables and five real-world problems. Results show that the proposed method has better performance than other latest algorithms.