A multi-population differential evolution with best-random mutation strategy for large-scale global optimization
Differential evolution (DE) is an efficient population-based search algorithm with good robustness, but it faces challenges in dealing with Large-Scale Global Optimization (LSGO). In this paper, we proposed an improved multi-population differential evolution with best-random mutation strategy (calle...
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| Veröffentlicht in: | Applied intelligence (Dordrecht, Netherlands) Netherlands), 2020-05, Vol.50 (5), p.1510-1526 |
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| creator | Ma, Yongjie Bai, Yulong |
| description | Differential evolution (DE) is an efficient population-based search algorithm with good robustness, but it faces challenges in dealing with Large-Scale Global Optimization (LSGO). In this paper, we proposed an improved multi-population differential evolution with best-random mutation strategy (called mDE-brM). The population is divided into three sub-populations based on the fitness values, each sub-population uses different mutation strategies and control parameters, individuals share different mutation strategies and control parameters by migrating among sub-populations. A novel mutation strategy is proposed, which uses the best individual and a randomly selected individual to generate base vector. The performance of mDE-brM is evaluated on the CEC 2013 LSGO benchmark suite and compared with 5 state-of-the-art optimization techniques. The results show that, compared with other contestant algorithms, mDE-brM has a competitive performance and better efficiency in LSGO. |
| doi_str_mv | 10.1007/s10489-019-01613-2 |
| format | Article |
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| subjects | Artificial Intelligence Computer Science Evolutionary computation Global optimization Machines Manufacturing Mechanical Engineering Mutation Optimization techniques Parameters Performance evaluation Population Populations Processes Search algorithms Strategy |
| title | A multi-population differential evolution with best-random mutation strategy for large-scale global optimization |
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