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Differential Evolution Improved with Adaptive Control Parameters and Double Mutation Strategies

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Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems (AsiaSim 2016, SCS AutumnSim 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 643))

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Abstract

Recently, differential evolution (DE) algorithm has attracted more and more attention as an excellent and effective approach for solving numerical optimization problems. However, it is difficult to set suitable mutation strategies and control parameters. In order to solve this problem, in this paper a dynamic adaptive double-model differential evolution (DADDE) algorithm for global numerical optimization is proposed, and dynamic random search (DRS) strategy is introduced to enhance global search capability of the algorithm. The simulation results of ten benchmark show that the proposed DADDE algorithm is better than several other intelligent optimization algorithms.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61573144, 61174040), Shanghai Commission of Science and Technology (Grant no. 12JC1403400), and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

    Jun Liu, Xiaoming Yin & Xingsheng Gu

Authors
  1. Jun Liu
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  2. Xiaoming Yin
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  3. Xingsheng Gu
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Corresponding author

Correspondence to Xingsheng Gu .

Editor information

Editors and Affiliations

  1. Beihang University, Beijing, China

    Lin Zhang

  2. Beihang University, Beijing, China

    Xiao Song

  3. Beihang University, Beijing, China

    Yunjie Wu

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Liu, J., Yin, X., Gu, X. (2016). Differential Evolution Improved with Adaptive Control Parameters and Double Mutation Strategies. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-10-2663-8_20

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  • DOI: https://doi.org/10.1007/978-981-10-2663-8_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2662-1

  • Online ISBN: 978-981-10-2663-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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