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Published in:
2015 | OriginalPaper | Chapter
Enhance Differential Evolution Algorithm Based on Novel Mutation Strategy and Parameter Control Method
Authors : Laizhong Cui, Genghui Li, Li Li, Qiuzhen Lin, Jianyong Chen, Nan Lu
Published in: Neural Information Processing
Publisher: Springer International Publishing
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Abstract
Differential evolution (DE) algorithm is a very effective and efficient approach for solving global numerical optimization problems. However, DE still suffers from some limitations. Moreover, the performance of DE is sensitive to its mutation strategy and associated parameters. In this paper, an enhanced differential evolution algorithm called EDE is proposed, which including a new mutation strategy and a new control method of parameters. Compared with other DE algorithms including four classical DE and two state-of-the-art DE variants on ten numerical benchmarks, the experiment results indicate that the performance of EDE is better than those of the other algorithms.