A Multi-objective Evolutionary Algorithm based on Hierarchical Grouping for Large-scale Multi-objective Optimization

Variable decomposition have been widely applied to large-scale multi-objective problems. Existing studies usually consume a large amount of computational resources in the decomposition phase. To this end, this paper proposes a multi-objective evolutionary algorithm based on hierarchical grouping (MOEA-HG). The algorithm contains a decomposition phase and an optimization phase. In the decomposition phase, diversity variables and convergence variables are first identified. Secondly, the concept of contribution is introduced. Convergence variables with equal contribution to the optimization objectives constitute a subcomponent. In the optimization stage, collaborative optimization is proposed to deal with convergence subcomponents and diversity subcomponents separately. Experimental results show that MOEA-HG consumes less computational resources in identifying variable interactions than other decomposition-based MOEAs. Furthermore, MOEA-HG has significant advantages over the five state-of-the-art MOEAs in terms of optimization performance.