A dual-population constrained multi-objective evolutionary algorithm with variable auxiliary population size

Abstract Constrained multi-objective optimization problems (CMOPs) exist widely in the real world, which simultaneously contain multiple constraints to be satisfied and conflicting objectives optimized. Therefore, challage addressing CMOPs is how better balance objectives. To remedy this issue, paper proposes a novel dual-population based constrained evolutionary algorithm solve CMOPs, two populations with different functions are employed. Specifically, main population considers both for solving original while auxiliary used only of without considering constraints. In addition, dynamic size reducing mechanism proposed, adjust population, so as reduce consumption computing resoruces later stage. Moreover, an independent external archive set store feasible solutions found by provide high-quality population. The experimental results on 55 benchmark show that proposed exhibits superior or at least competitive performance compared other state-of-the-art algorithms.