| Autor: |
Xiang-wei Zheng, Xian-cui Xiao, Ya-zhou Li |
| Rok vydání: |
2015 |
| Předmět: |
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| Zdroj: |
The 27th Chinese Control and Decision Conference (2015 CCDC). |
| DOI: |
10.1109/ccdc.2015.7162583 |
| Popis: |
Group Search Optimizer (GSO) is a swarm intelligence algorithm inspired from animal's foraging behavior. The algorithm demonstrated its obvious superiority in solving complex engineering problems. Based on the strategy of divide-and-conquer and cooperative coevolution framework, a Cooperative Coevolutionary Multi-objective Group Search Optimizer (CMOGSO) is proposed in this paper. In CMOGSO, multi-objective optimization problems are decomposed according to their decision variables and are optimized by corresponding sub-groups respectively. Collaborators are selected randomly from archive and employed to construct context vectors in order to evaluate the members in sub-groups. Experimental results demonstrate that CMOGSO can more effectively and efficiently solve multi-objective optimization problems compared with other evolutionary multi-objective optimizers. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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