A novel multi-population evolutionary algorithm based on hybrid collaboration for constrained multi-objective optimization

Abstract

Multi-population-based methods are widely employed for solving constrained multiobjective optimization problems (CMOPs). The population collaboration strategy is a critical part of multi-population algorithms, and different collaboration strategies perform well on different complex CMOPs. However, these single-population collaboration strategies are still challenging to adapt to various CMOPs with different characteristics. To address this issue, we propose a novel tri-population hybrid collaboration evolutionary algorithm called TP-HCEA, which includes a constraint-relaxed population (denoted as mainpop), a constraint-ignored auxiliary population (denoted as auxpop1), and an auxiliary population (denoted as auxpop2) for the original CMOP, to search optimal solutions in the feasible region. Specifically, due to the different complementarities of the two auxiliary populations, mainpop collaborates with auxpop1 and auxpop2 in a dynamic choice between strong and weak cooperation. The effectiveness of TP-HCEA is validated through comparative analysis with seven state-of-the-art algorithms in four CMOP benchmark suites and nine real-world problems.