A Surrogate-Assisted Expensive Constrained Multi-Objective Optimization Algorithm Based on Adaptive Switching of Acquisition Functions
Haofeng Wu, Qingda Chen, Yaochu Jin, Jinliang Ding, Tianyou Chai
Abstract
Expensive constrained multi-objective optimization problems (ECMOPs) present a significant challenge to surrogate-assisted evolutionary algorithms (SAEAs) in effectively balancing optimization of the objectives and satisfaction of the constraints with complex landscapes, leading to low feasibility, poor convergence and insufficient diversity. To address these issues, we design a novel algorithm for the automatic selection of two acquisition functions, thereby taking advantage of the benefits of both using and ignoring constraints. Specifically, a multi-objective acquisition function that ignores constraints is proposed to search for problems whose unconstrained Pareto-optimal front (UPF) and constrained Pareto-optimal front (CPF) are similar. In addition, another constrained multi-objective acquisition function is introduced to search for problems whose CPF is far from the UPF. Following the optimization of the two acquisition functions, two model management strategies are proposed to select promising solutions for sampling new solutions and updating the surrogates. Any multi-objective evolutionary algorithm (MOEA) for solving non-constrained and constrained multiobjective optimization problems can be integrated into our algorithm. The performance of the proposed algorithm is evaluated on five suites of test problems, one benchmark-suite of real-world constrained multi-objective optimization problems (RWCMOPs) and a real-world optimization problem. Comparative results show that the proposed algorithm is competitive against state-of-the-art constrained SAEAs.