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A Uniform Approach for the Comparison of Opposition-Based Learning

  • Qingzheng XuEmail author
  • Heng Yang
  • Na Wang
  • Rong Fei
  • Guohua Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

Although remarkable progress has been made in the application of opposition-based learning in recent years, the complete theoretical comparison is seldom reported. In this paper, an evaluation function of opposition strategy is defined and then a uniform evaluation approach to compute the mean minimum Euclidean distance to the optimal solution is proposed for one dimensional case. Thus different opposition strategies can be compared easily by means of the mathematical expectation of these evaluation functions. Theoretical analysis and simulation experiments can support each other, and also show the effectiveness of this method for sampling problems.

Keywords

Opposition-Based learning Performance comparison Evaluation function Uniform approach 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Nos. 61305083 and 61603404), Shaanxi Science and Technology Project (No. 2017CG-022) and Xi’an Science and Research Project (No. 2017080CG/RC043).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Qingzheng Xu
    • 1
    Email author
  • Heng Yang
    • 1
  • Na Wang
    • 1
  • Rong Fei
    • 2
  • Guohua Wu
    • 3
  1. 1.College of Information and CommunicationNational University of Defense TechnologyXi’anChina
  2. 2.School of Computer Science and EngineeringXi’an University of TechnologyXi’anChina
  3. 3.College of Systems EngineeringNational University of Defense TechnologyChangshaChina

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