A Surrogate-Assisted Offspring Generation Method for Expensive Multi-objective optimization Problems

Abstract

Surrogate-assisted multi-objective evolutionary algorithms have been commonly used to solve multi-objective expensive problems. In this paper, we investigate whether the surrogate-assisted offspring generation method can improve the optimization efficiency of multi-objective evolutionary algorithms. We first construct a surrogate model for each objective function. After that, some candidate solutions from the surrogate models are used to produce promising offspring for the multi-objective evolutionary algorithm. In addition, a prescreening criterion based on reference vectors and the nondominated rank is used to select the surviving offspring and exactly evaluated individuals. The pre-screening criterion can ensure the diversity and convergence of the offspring, and reduce function evaluations. Benchmark problems with their dimensions varying from 8 to 30 are used to test the effects of the surrogate-assisted offspring generation method under the framework of using the pre-screening criterion. Experimental results show that using the candidate solutions from surrogate models can enhance the performance of its basic algorithm on most of the problems.