针对重叠变量难题，提出两阶段协同进化框架，显著提升大规模优化性能。
A Novel Two-Phase Cooperative Co-evolution Framework for Large-Scale Global Optimization with Complex Overlapping
- 基于数学性质分解重叠问题，实现更精准的子空间划分。
- 在复杂重叠场景下，性能超越现有算法，提升明显。
- 开源框架支持自定义测试，适合优化研究者使用。
协同进化通过分解问题空间，是解决大规模全局优化问题的主要方法。当子空间互不重叠时，该类算法在效率与效果上显著优于非分解算法。然而，重叠变量的存在使分解过程复杂化，严重影响协同进化性能。本文提出一种新型两阶段协同进化框架，以应对具有复杂重叠结构的大规模全局优化问题。框架内嵌基于数学性质的重叠问题分解方法，并引入可定制化的重叠问题基准，扩展现有评测体系，促进实验研究。大量实验证明，基于该框架实现的算法显著优于现有方法。结果揭示了重叠问题的特性，凸显了协同进化与非分解算法在不同场景下的优劣。相关代码已开源：https://github.com/GMC-DRL/HCC。
原文摘要 · Abstract (English)
Cooperative Co-evolution, through the decomposition of the problem space, is a primary approach for solving large-scale global optimization problems. Typically, when the subspaces are disjoint, the algorithms demonstrate significantly both effectiveness and efficiency compared to non-decomposition algorithms. However, the presence of overlapping variables complicates the decomposition process and adversely affects the performance of cooperative co-evolution. In this study, we propose a novel two-phase cooperative co-evolution framework to address large-scale global optimization problems with complex overlapping. An effective method for decomposing overlapping problems, grounded in their mathematical properties, is embedded within the framework. Additionally, a customizable benchmark for overlapping problems is introduced to extend existing benchmarks and facilitate experimentation. Extensive experiments demonstrate that the algorithm instantiated within our framework significantly outperforms existing algorithms. The results reveal the characteristics of overlapping problems and highlight the differing strengths of cooperative co-evolution and non-decomposition algorithms. Our work is open-source and accessible at: https://github.com/GMC-DRL/HCC.
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