A Surrogate-Assisted Coevolutionary Algorithm for Expensive Constrained Multiobjective Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) are often used for expensive constrained multi-objective optimization problems (CMOPs). However, they exhibit poor convergence and diversity on complex CMOPs because they may consume more evaluations for approximating the constrained Pareto Front (CPF), i.e., the infeasible regions far from the CPF. To address this issue, we propose a surrogate-assisted coevolution-ary algorithm, which improves the convergence and diversity performance by searching on auxiliary problems together with the original CMOP. Firstly, we construct a surrogate model for each objective and constraint function. The primary population searches on both objective and constrained surrogate models, which approximate for the original constrained problem. The secondary population searches on an auxiliary problem, which is a multi-objective optimization problem (MOP) approximated by objective surrogate models. The two populations exchange information to enhance the convergence performance of the primary population. To improve the feasibility, the auxiliary problem is adopted with a certain probability. A novel infill criterion is proposed to guide the selection of high-quality solutions for updating the surrogate model, which relies on the feasible ratio of evaluated solutions. The performance of the proposed algorithm is evaluated on multi-objective and many-objective benchmarks to demonstrate its competitiveness.