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New mutation strategies of differential evolution based on clearing niche mechanism

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Abstract

Although differential evolution (DE) algorithms have been widely proposed for tackling various of problems, the trade-off among population diversity, global and local exploration ability, and convergence rate is hard to maintain with the existing strategies. From this respective, this paper presents some new mutation strategies of DE by applying the clearing niche mechanism to the existing mutation strategies. Insteading of using random, best or target individuals as base vector, the niche individuals are utilized in these strategies. As the base vector is from a subpopulation, which is made up of the best individuals in each niche, the base vector can be guided by the global or local best ones. This mechanism is beneficial to the balance among population diversity, search capability, and convergence rate of DE, since it can both enhance the population diversity and search capability. Extensive experimental results indicate that the proposed strategies based on clearing niche mechanism can effectively enhance DE’s performance.

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Acknowledgments

This research has been supported by National Natural Science Fundation of China under Grant Nos. 71103163, 71573237; New Century Excellent Talents in University of China under Grant No. NCET-13-1012; Research Foundation of Humanities and Social Sciences of Ministry of Education of China No. 15YJA630019; Special Funding for Basic Scientific Research of Chinese Central University under Grant Nos. CUG120111, CUG110411, G2012002A, CUG140604, CUG160605; Open Foundation for the Research Center of Resource Environment Economics in China University of Geosciences (Wuhan); Structure and Oil Resources Key Laboratory Open Project of China under Grant No. TPR-2011-11.

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Authors and Affiliations

  1. College of Economics and Management, China University of Geosciences, Wuhan, 430074, China

    Yanan Li, Haixiang Guo, Xiao Liu, Yijing Li & Wenwen Pan

  2. Research Center for Digital Business Management, China University of Geosciences, Wuhan, 430074, China

    Yanan Li, Haixiang Guo, Xiao Liu, Yijing Li & Wenwen Pan

  3. Mineral Resource Strategy and Policy Research Center of China University of Geosciences, Wuhan, 430074, China

    Haixiang Guo

  4. The Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Haixiang Guo

  5. Department of Industrial Engineering, Business Administration and Statistic E.T.S Industrial Engineering, Universidad Politécnica de Madrid C/José Gutiérrez Abascal 2, 20086, Madrid, Spain

    Bing Gong

  6. Department of Computing, Unitec Institute of Technology, Private Bag 92025, Auckland, New Zealand

    Shaoning Pang

Authors
  1. Yanan Li
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  2. Haixiang Guo
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  3. Xiao Liu
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Corresponding author

Correspondence to Haixiang Guo.

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Conflict of interest

Author Yanan Li declares that she has no conflict of interest. Author Haixiang Guo declares that he has no conflict of interest. Author Xiao Liu declares that she has no conflict of interest. Author Yijing Li declares that she has no conflict of interest. Author Wenwen Pan declares that she has no conflict of interest. Author Bing Gong declares that he has no conflict of interest. Author Shaoning Pang declares that he has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by Y. Jin.

Appendix

Appendix

See Table 19.

Table 19 Ten-unit generator characteristics
Full size table

The transmission loss formula coefficients of ten-unit system are:

$$\begin{aligned} B= & {} \left[ {\begin{array}{l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l@{\quad }l} 0.000049&{}0.000014&{}0.000015&{}0.000015&{}0.000016&{}0.000017&{}0.000017&{}0.000018&{}0.000019&{}0.000020\\ 0.000014&{}0.000045&{}0.000016&{}0.000016&{}0.000017&{}0.000015&{}0.000015&{}0.000016&{}0.000018&{}0.000018\\ 0.000015&{}0.000016&{}0.000039&{}0.000010&{}0.000012&{}0.000012&{}0.000014&{}0.000014&{}0.000016&{}0.000016\\ 0.000015&{}0.000016&{}0.000010&{}0.000040&{}0.000014&{}0.000010&{}0.000011&{}0.000012&{}0.000014&{}0.000015\\ 0.000016&{}0.000017&{}0.000012&{}0.000014&{}0.000035&{}0.000011&{}0.000013&{}0.000013&{}0.000015&{}0.000016\\ 0.000017&{}0.000015&{}0.000012&{}0.000010&{}0.000011&{}0.000036&{}0.000012&{}0.000012&{}0.000014&{}0.000015\\ 0.000017&{}0.000015&{}0.000014&{}0.000011&{}0.000013&{}0.000012&{}0.000038&{}0.000016&{}0.000016&{}0.000018\\ 0.000018&{}0.000016&{}0.000014&{}0.000012&{}0.000013&{}0.000012&{}0.000016&{}0.000040&{}0.000015&{}0.000016 \\ 0.000019&{}0.000018&{}0.000016&{}0.000014&{}0.000015&{}0.000014&{}0.000016&{}0.000015&{}0.000042&{}0.000019\\ 0.000020&{}0.000018&{}0.000016&{}0.000015&{}0.000016&{}0.000015&{}0.000018&{}0.000016&{}0.000019&{}0.000044\\ \end{array}}\right] \end{aligned}$$

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Li, Y., Guo, H., Liu, X. et al. New mutation strategies of differential evolution based on clearing niche mechanism. Soft Comput 21, 5939–5974 (2017). https://doi.org/10.1007/s00500-016-2318-4

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