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Enhance Differential Evolution Algorithm Based on Novel Mutation Strategy and Parameter Control Method

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

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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.

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Acknowledgments

This work was supported in part by National Natural Science Foundation of China (Grant no. 61402294, 61170283 and 61402291), National High-Technology Research and Development Program (863 Program) of China (Grant no. 2013AA01A212), Ministry of Education in the New Century Excellent Talents Support Program (Grant no. NCET-12-0649), Guangdong Natural Science Foundation (Grant no. S2013040012895), Foundation for Distinguished Young Talents in Higher Education of Guangdong, China (Grant no. 2013LYM_0076 and 2014KQNCX129), Major Fundamental Research Project in the Science and Technology Plan of Shenzhen (Grant no. JCYJ20130329102017840, JCYJ20130329102032059, JCYJ20140418095735608 and JCYJ20140828163633977).

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, Guangdong, China

    Laizhong Cui, Genghui Li, Li Li, Qiuzhen Lin, Jianyong Chen & Nan Lu

Authors
  1. Laizhong Cui
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  2. Genghui Li
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  3. Li Li
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  4. Qiuzhen Lin
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  5. Jianyong Chen
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  6. Nan Lu
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Corresponding author

Correspondence to Laizhong Cui .

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Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Cui, L., Li, G., Li, L., Lin, Q., Chen, J., Lu, N. (2015). Enhance Differential Evolution Algorithm Based on Novel Mutation Strategy and Parameter Control Method. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_70

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  • DOI: https://doi.org/10.1007/978-3-319-26532-2_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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