A surrogate-assisted evolutionary algorithm with hypervolume triggered fidelity adjustment for noisy multiobjective integer programming

Abstract

Although surrogate-assisted evolutionary algorithms (SAEAs) have been widely developed to address computationally expensive multi-objective optimization problems (MOPs), they still encounter difficulties in solving the expensive and noisy combinatorial MOPs. To this end, we propose a novel SAEA to handle this kind of problem. In the proposed algorithm, the averaging method is used to denoise. To balance the conflict between the time cost and the effect of noises, multi-fidelity surrogate models are constructed according to the averaged evaluation results. The number of independent repeated evaluations represents the fidelity level of surrogate models. In the optimization process, the hypervolume indicator is employed as a trigger to determine whether the fidelity level should be increased. In addition, a lightweight local search method, the semi-variable neighborhood search, is proposed to improve the global search efficiency of the proposed algorithm in discrete decision spaces. Experimental results show that our proposed algorithm achieves competitive performance on most benchmark problems.