A Surrogate-Ensemble Assisted Coevolutionary Algorithm for Expensive Constrained Multi-Objective Optimization Problems

Wenji Li, Ruitao Mai, Pengxiang Ren, Zhaojun Wang, Qinchang Zhang, Ning Xu, Biao Xu, Zhun Fan, Zhifeng Hao

Abstract: In real-world applications, there are some constrained multi-objective problems where the evaluation of objectives is expensive and the evaluation of constraints is cheap. Currently, few studies have focused on solving expensive constrained multi-objective optimization problems (ECMOPs), and they usually assume that the constraints of ECMOPs are also expensive. In this paper, we propose a surrogate-ensemble assisted coevolutionary algorithm (SEACoEA) for ECMOPs with inexpensive constraint evaluation. First, a feasible sampling strategy is designed to initialize the population in the feasible regions. Next, two populations are set to optimize the original ECMOP and the problem without considering constraints, respectively. To improve the search efficiency, we redesigned the objective function of the surrogate-ensemble model. Finally, a new infill strategy is proposed to select candidate individuals from each population for real evaluation. Experimental results show that the proposed algorithm performs significantly better on most MW problems compared to several state-of-the-art algorithms.