A large-scale multiobjective evolutionary algorithm with overlapping decomposition and adaptive reference point selection

doi:10.1007/s10489-023-04596-3

2023-06-06

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DOI：10.1007/s10489-023-04596-3

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摘要

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Many large-scale multiobjective optimization problems with large decision space hinder the convergence search of evolutionary algorithms in various practical applications. Using the divide-and-conquer strategy to decompose the large-scale multiobjective problem into some subproblems and collaborative optimization is an effective strategy. However, the interactions between decision variables may cause many indirect interactions, which make complex high-dimensional problems impossible to decompose successfully using existing decomposition techniques. This paper proposes a multiobjective evolutionary algorithm with overlapping decomposition and adaptive reference point selection (MOEA-ODAR) for solving large-scale multiobjective problems. First, a decision variable overlap decomposition approach is suggested to group decision variables into several exclusive subcomponents. An adaptive resource allocation ensemble optimization method is then proposed to allocate corresponding resources to subcomponents with different structures. Finally, an adaptive reference point selection method based on Pareto shape estimation is designed to optimize the specific subcomponents. The theoretical analysis of the correctness of overlapping decomposition decision variables and collaborative optimization is presented. It is compared with the newly proposed excellent large-scale multiobjective optimization algorithm on many test problems. The experimental results show that the proposed algorithm performs better in terms of convergence, population distributivity, and computational efficiency. In addition, the superiority of the algorithm on large-scale many-objective problems is verified.

Keyword:

Large-scale multiobjective optimization

Overlap decomposition

Adaptive reference point selection

Ensemble optimization