Scholarlink

<p>The high-dimensional decision space of large-scale multi-objective optimization poses challenges to evolutionary algorithms, which leads to potential trapping in local optima. This paper proposes a brain information decomposition mechanism inspired large-scale multi-objective evolutionary algorithm (IDLMEA). Three strategies are developed. 1) The redundant information subpopulation division strategy selects high-quality solutions using shift-based density estimation, which determines the global optimal information. 2) The synergistic information subpopulation division strategy identifies exploration direction information by dividing solutions using local sensitive hashing. 3) The redundant and synergistic information-based reproduction strategy generates offspring for effective exploration of the search space. The Friedman test values of IDLMEA for inverted generational distance on LSMOP and IMF benchmarks outperform ten competitors by at least 25.64% and 4.04%, respectively.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A brain information decomposition mechanism inspired evolutionary algorithm for large-scale multi-objective optimization

  • 期刊论文

  • 发表日期：

    2025年12月13日

查看全文
  • Tongxuan Wu,
  • Junzhong Ji,
  • Cuicui Yang

摘要

The high-dimensional decision space of large-scale multi-objective optimization poses challenges to evolutionary algorithms, which leads to potential trapping in local optima. This paper proposes a brain information decomposition mechanism inspired large-scale multi-objective evolutionary algorithm (IDLMEA). Three strategies are developed. 1) The redundant information subpopulation division strategy selects high-quality solutions using shift-based density estimation, which determines the global optimal information. 2) The synergistic information subpopulation division strategy identifies exploration direction information by dividing solutions using local sensitive hashing. 3) The redundant and synergistic information-based reproduction strategy generates offspring for effective exploration of the search space. The Friedman test values of IDLMEA for inverted generational distance on LSMOP and IMF benchmarks outperform ten competitors by at least 25.64% and 4.04%, respectively.