中文

Journal of Applied Sciences ›› 2011, Vol. 29 ›› Issue (3): 308-315.

• Signal and Information Processing • Previous Articles     Next Articles

Opposition-Based Differential Evolution Using the Current Optimum for Function Optimization

XU Qing-zheng1;2, WANG Lei1, HE Bao-min1, WANG Na2   

  1. 1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
    2. Department of Military Electronic Engineering, Xi’an Communication Institute, Xi’an 710106, China
  • Received:2010-12-07 Revised:2011-04-10 Online:2011-05-26 Published:2011-05-26

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Abstract:

When the global optimum is not located at the geometric center of the domain, the opposite numbers may lapse from the global optimum, leading to poor performance of opposition-based differential evolution. A novel opposition-based learning strategy using the current optimum is introduced, and it is combined with differential evolution for function optimization. The optimum in the current generation is served as a symmetry point between an estimate and the corresponding opposite estimate, resulting in a high rate of opposite population usage. Experiments results clearly show that the proposed algorithm can significantly improve the performance due to the opposite numbers. Additionally, an enhanced version of opposition-based
differential evolution is proposed to reveal ideal and perfect results using opposition-based learning.

Key words: differential evolution, opposition-based learning, current optimum, function optimization

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