A classification surrogate-assisted multi-objective evolutionary algorithm for expensive optimization

Abstract

Surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) have been developed for solving expensive optimization problems. According to the roles that the surrogate models play in SAMOEAs, they can be divided into two categories: prediction-based and classification-based algorithms. Though prediction-based SAMOEAs are the mainstream methods, classification-based ones are gaining their fast developments. In this article, a classification surrogate-assisted multi-objective evolutionary algorithm (CSA-MOEA) is proposed for expensive optimization. The algorithm adopts a classification tree as the surrogate model to predict promising offsprings, which may be non-dominated solutions with good convergence. Then based on two effective infilling strategies, some of these promising individuals are added to the sample archive. By repeating the above steps iteratively, valuable solutions can be obtained. To evaluate the performance of CSA-MOEA, it is compared with several state-of-the-art surrogate-assisted evolutionary algorithms on three sets of multi-objective optimization test problems and an engineering shape optimization problem. The experimental results demonstrate the competitiveness of CSA-MOEA.