A constrained multi-objective evolutionary strategy based on population state detection

The difficulty of solving constrained multi-objective optimization problems (CMOPs) using evolutionary algorithms is to balance constraint satisfaction and objective optimization while fully considering the diversity of the solution set. Many CMOPs with disconnected feasible subregions make it difficult for algorithms to search for all feasible nondominated solutions. To address these issues, we propose a population state detection strategy (PSDS) and a restart scheme to determine whether the environmental selection strategy needs to be changed based on the situation of population. When the population converges in the feasible region, the unconstrained environmental selection allows the population to cross the current feasible region. When the population converging outside the feasible region, all constraints will be considered in the environmental selection to select the population for the feasible region. In addition, the restart scheme will use reinitialization to make the population jump out of unprofitable iterations. The proposed algorithm enhances the search ability through the detection strategy and provides more diversity by reinitializing the population. The experimental results on four constraint test suites with various features have demonstrated that the proposed algorithm had better or competitive performance against other state-of-the-art constrained multi-objective algorithms.