系统仿真学报 ›› 2026, Vol. 38 ›› Issue (4): 974-987.doi: 10.16182/j.issn1004731x.joss.25-0216

• 论文 • 上一篇    下一篇

基于多区域动态分组的大规模多目标进化算法

梁斌豪, 魏静萱, 梁沨琴   

  1. 西安电子科技大学 计算机科学与技术学院,陕西 西安 710126
  • 收稿日期:2025-03-21 修回日期:2025-05-16 出版日期:2026-04-20 发布日期:2026-04-22
  • 通讯作者: 魏静萱
  • 第一作者简介:梁斌豪(1999-),男,硕士生,研究方向为大规模多目标优化。
  • 基金资助:
    国家自然科学基金(62272367)

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

摘要:

大规模多目标优化问题的决策变量维度可达数百上千维。针对现有的基于决策变量分析的大规模多目标进化算法通常需要消耗大量的计算资源用于分组,且未考虑收敛性相关变量和多样性相关变量之间的相互作用,提出一种多区域自适应动态分组的大规模多目标进化算法。利用高斯混合 模型将决策空间划分为多个区域;在每个区域内,为每个决策变量构造特征向量,并使用谱聚类算法进行分组。为验证算法的有效性,将其与不同算法在140个大规模基准测试问题上进行了实验对比。结果表明:该算法在收敛性和多样性指标上具有更好的性能。

关键词: 大规模多目标优化, 进化算法, 动态分组, 决策变量分析, 问题转换

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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