Journal of System Simulation ›› 2026, Vol. 38 ›› Issue (4): 974-987.doi: 10.16182/j.issn1004731x.joss.25-0216

• Papers • Previous Articles     Next Articles

Large-scale Multi-objective Evolutionary Algorithm Based on Multi-region Dynamic Grouping

Liang Binhao, Wei Jingxuan, Liang Fengqin   

  1. School of Computer Science and Technology, Xidian University, Xi'an 710126, China
  • Received:2025-03-21 Revised:2025-05-16 Online:2026-04-20 Published:2026-04-22
  • Contact: Wei Jingxuan

Abstract:

The decision variable dimension of large-scale multi-objective optimization problems can reach hundreds or even thousands. For existing large-scale multi-objective evolutionary algorithms based on decision variable analysis, which usually consume a large amount of computational resources for grouping and fail to consider the interactions between convergence-related variables and diversity-related variables, a large-scale multi-objective evolutionary algorithm based on multi-region adaptive dynamic grouping was proposed. The algorithm employed a Gaussian mixture model to partition the decision space into multiple regions; within each region, feature vectors were constructed for each decision variable, and spectral clustering was utilized to perform grouping. To validate the effectiveness of the algorithm, it was compared experimentally with different algorithms on 140 large-scale benchmark test problems. The results demonstrate that the proposed algorithm exhibits better performance in terms of both convergence and diversity metrics.

Key words: large-scale multi-objective optimization, evolutionary algorithm, dynamic grouping, decision variable analysis, problem transformation

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