A Multipopulation Evolutionary Algorithm Using New Cooperative Mechanism for Solving Multiobjective Problems With Multiconstraint

In science and engineering, multiobjective optimization problems (MOPs) usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This article aims to solve the challenges brought by multiple complex constraints. First, this article analyzes the relationship between single-constrained Pareto front (SCPF) and their common Pareto front (PF) subconstrained PF (SubCPF). Next, we discussed the SCPF, SubCPF, and unconstraint PF (UPF)’s help to solve constraining PF (CPF). Then, further discusses what kind of cooperation should be used between multiple populations constrained multiobjective optimization algorithm (CMOEA) to better deal with multiconstrained MOPs (mCMOPs). At the same time, based on the discussion in this article, we propose a new multipopulation CMOEA called MCCMO, which uses a new cooperation mechanism. MCCMO uses C+2 (C is the number of constraints) populations to find the UPF, SCPF, and SubCPF at an appropriate time. Furthermore, MCCMO uses the newly proposed activation dormancy detection (ADD) to accelerate the optimization process and uses the proposed combine occasion detection (COD) to find the appropriate time to find the SubCPF. The performance on 32 mCMOPs and real-world mCMOPs shows that our algorithm can obtain competitive solutions on MOPs with multiple constraints.