Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 120-123.

• Intelligent Computing • Previous Articles     Next Articles

Hybrid Particle Swarm Optimization with Multiply Strategies

YU Wei-wei1,XIE Cheng-wang2   

  1. School of Software,Beijing University of Technology,Beijing 100124,China1
    Science Computing and Intelligent Information Processing of Guangxi Higher Education Key Laboratory,Guangxi Teachers Education University,Nanning 530023,China2
  • Online:2018-06-20 Published:2018-08-03

Abstract: A hybrid particle swarm optimization with multiply strategies (HPSO) was proposed to solve the problem of being easy to get into the local optimum and slow convergence speed for particle swarm optimization algorithm(PSO) in dealing with some complicated optimization problems.The HPSO uses the opposition-based learning strategy to genera-te the opposition-based solutions,which enlarges the search range of particle swarm,and enhances the global exploration ability of the algorithm.At the same time,in order to jump out of the local optimum,the HPSO performs Cauchy mutation on some poorer particles to generate individuals that are far from the local optimum,and the differential evolution (DE) mutation is employed to remain individuals to improve the capacity of local exploitation.The above strategies are combined to balance the abilities of global exploration and local exploitation,which are expected to solve some hard optimization problems better.The HPSO and other three well-known PSOs were compared on 10 benchmark test instances experimentally.The results show that the HPSO performs significant advantages over the compared algorithms in the solution accuracy and the convergence speed.

Key words: Opposition-based learning, Particle swam optimization, Cauchy mutation, Differential evolution

CLC Number: 

  • TP301
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