A Batched Expensive Multiobjective Optimization Based on Constrained Decomposition with Grids

Abstract

A batched constrained decomposition with grids (BCDG) is proposed for expensive multiobjective optimization problems. In this algorithm, each objective function is approximated by a Gaussian process model and CDG-MOEA is used to optimize a candidate population. Finally, we use Hypervolume Indicator to select some better points from the candidate population for evaluation. In the process of CDG-MOEA optimizing candidate solutions and using Hypervolume Indicator to select candidate solutions for evaluation, we use Gaussian process lower confidence bound criteria to consider the uncertainty of Gaussian process prediction. Experimental study on some special test problems shows that BCDG can effectively solve some special expensive multiobjective optimization problems.