Dual-Population Evolution Based Dynamic Constrained Multiobjective Optimization With Discontinuous and Irregular Feasible Regions

Dynamic constrained multiobjective optimization problems include irregular and discontinuous feasible regions, segmented true Pareto front, and dynamic environments. To address these problems, we design a dynamic constrained multiobjective optimization algorithm based on dual-population evolution. This algorithm includes two populations, P₁ and P₂, based on the feasibility of solutions. It utilizes valuable information from infeasible solutions to drive the populations toward the feasible regions and the true Pareto front. At the same time, we propose a mating selection operator to facilitate information exchange between populations and generate promising offspring solutions. To respond to environmental changes, we design a strategy that combines new solutions obtained by the sampling-selection-resampling method and updated old ones, rapidly generating a promising population in a new environment. Additionally, we also design a test suit that can effectively present the discontinuous feasible regions and the irregular changes of true Pareto front in practical appcation problems. The results from experiments demonstrate the efficacy of the test suit, and the proposed algorithm exhibits competitiveness compared to other algorithms.