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A General Selection Method for Mutation Strategy in Differential Evolution

  • Dahai XiaEmail author
  • Song Lin
  • Meng Yan
  • Caiquan Xiong
  • Yuanxiang Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)

Abstract

How to balance exploration and exploitation is a key issue for evolution algorithm including differential algorithm (DE). Many researchers propose various improved mutation strategies to solve this issue for DE. Most of them can be classified as deterministic rules. That is to say, they select individuals according to predetermined methods and so the balance is static. However, different evolution stages require different balance between exploration and exploitation. In order to solve this problem, a general selection method named adaptive stochastic ranking based mutation strategies in DE(ASR-DE). In ASR-DE, it uses stochastic ranking method to rank all individuals according to their contribution in exploration and exploitation. The parameter P\(_f\) in stochastic ranking is adaptive controlled by a transform version of success rate. The individuals with the smaller ranking are more likely to be selected. 28 functions of CEC2013 is used here to verify the validity of testing method. The test results show that ASR-DE improves the standard DE and improved DE comparing with other methods.

Keywords

Different evolution Exploration and exploitation Mutation operator Adaptive stochastic ranking 

Notes

Acknowledgement

Supported by National Key Research and Development Program of China under grant number 2017YFC1405403, and National Natural Science Foundation of China under grant number 6107505961300127, and Green Industry Technology Leding Project (product development category) of Hubei University of Technology under grant number CPYF2017008, and Doctor’s program of Hubei University of Technology under grant number BSQD2017047, BSQD2017045.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Dahai Xia
    • 1
    • 3
    Email author
  • Song Lin
    • 2
  • Meng Yan
    • 1
  • Caiquan Xiong
    • 1
  • Yuanxiang Li
    • 3
  1. 1.Computer SchoolHubei University of TechnologyWuhanChina
  2. 2.Strategic Teaching and Research SectionNaval Command CollegeNanjingChina
  3. 3.College of Computer ScienceWuhan UniversityWuhanChina

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