A Novel Two-Phase Cooperative Co-evolution Framework for Large-Scale Global Optimization with Complex Overlapping
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916664468045824 |
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| author | Qiu, Wenjie Guo, Hongshu Ma, Zeyuan Gong, Yue-Jiao |
| author_facet | Qiu, Wenjie Guo, Hongshu Ma, Zeyuan Gong, Yue-Jiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Novel Two-Phase Cooperative Co-evolution Framework for Large-Scale Global Optimization with Complex Overlapping Qiu, Wenjie Guo, Hongshu Ma, Zeyuan Gong, Yue-Jiao Neural and Evolutionary Computing Artificial Intelligence 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. |
| title | A Novel Two-Phase Cooperative Co-evolution Framework for Large-Scale Global Optimization with Complex Overlapping |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2503.21797 |