An ensemble dual model assisted MOEA/D for tackling medium scale expensive multiobjective optimization

Abstract

Many surrogate-assisted evolutionary algorithms (SAEAs) have been developed to address expensive multi-objective optimization problems (EMOPs). However, existing research primarily focuses on low-dimensional EMOPs. In this article, we propose an ensemble dual model-assisted multi-objective evolutionary algorithm based on decomposition to tackle medium-scale EMOPs. The proposed approach includes two key insights. First, a new ensemble strategy is proposed to improve both the prediction ability and computation efficiency of surrogates in tackling medium-scale EMOPs. Second, we develop an improved computing resource allocation strategy and an operator pool to enhance convergence capabilities. The improved computing resource allocation strategy gives more computing resources to the subproblems with poor fitness values, while the operator pool is used to generate promising offspring set in higher dimensions. Experimental results from three sets of expensive multi-objective test suites demonstrate that our proposed algorithm significantly outperforms seven compared SAEAs.