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Comparison of Differential Evolution Algorithms on the Mapping Between Problems and Penalty Parameters

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10385))

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Abstract

Penalty parameters play a key role when adopting the penalty function method for solution ranking. In the previous study, a corresponding relationship between the constrained optimization problems and the penalty parameters was constructed. This paper tries to verify whether the relationship is related with the evolutionary algorithms (EAs), i.e., how the EAs influence the relationship. Two differential evolution algorithms are taken as an example. Experimental results confirm the influence and show that an improved EA will enlarge the available value of corresponding penalty parameter, especially for the intermittent relationship. The findings also prove that EA can make up the shortcoming of constraint handling techniques to some extent.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 71371142, Shanghai Young Teachers’ Training Program under Grants ZZslg15087.

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Authors and Affiliations

  1. Shanghai-Hamburg College, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Chengyong Si, Jianqiang Shen & Xuan Zou

  2. College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Lei Wang

Authors
  1. Chengyong Si
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  2. Jianqiang Shen
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  3. Xuan Zou
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  4. Lei Wang
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Corresponding author

Correspondence to Chengyong Si .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Si, C., Shen, J., Zou, X., Wang, L. (2017). Comparison of Differential Evolution Algorithms on the Mapping Between Problems and Penalty Parameters. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_46

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  • DOI: https://doi.org/10.1007/978-3-319-61824-1_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61823-4

  • Online ISBN: 978-3-319-61824-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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