Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 124-129.

• Intelligent Computing • Previous Articles     Next Articles

Group Search Optimization with Opposition-based Learning and Differential Evolution

ZOU Hua-fu1,XIE Cheng-wang2,ZHOU Yang-ping1,WANG Li-ping3   

  1. Information Engineering College,Jiangxi Vocational College of Industry & Engineering,Pingxiang,Jiangxi 337055,China1
    Science Computing and Intelligent Information Processing of Guangxi Higher Education Key Laboratory,Guangxi Teachers Education University,Nanning 530023,China2
    School of Information and Computer Engineering,Pingxiang University,Pingxiang,Jiangxi 337055,China3
  • Online:2018-06-20 Published:2018-08-03

Abstract: In general,the standard group search optimization algorithm (GSO) is easy to fall into the local optimum and its convergence speed is slow when solving some complex optimization problems.A group search optimization algorithm based on opposition-based leaning and differential evolution (OBDGSO) was proposed in this paper.The OBDGSO uses the opposition-based learning operator to generate the opposite population to expand the global exploration range.In addition,the operator of differential evolution (DE) is utilized to perform local exploitation to improve the solution accuracy.These two strategies are integrated into the GSO to better balance the abilities of the global convergence and local search.The OBDGSO is tested on 12 benchmark functions along with four other peering algorithms,and the experimental results show that the OBDGSO has significant performance advantages in solution accuracy and convergence speed.

Key words: Opposition-based learning, Differential evolution, Group search optimizationalgorithm

CLC Number: 

  • TP301
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