1. School of Software,Jiangxi University of Science and Technology,Nanchang 330013 2.Institute of Higher Education,Jiangxi Agricultural University,Nanchang 330045
Abstract Based on the classic particle swarm optimization (PSO) algorithm,a fractal evolutionary particle swarm optimization(FEPSO)is proposed . In FEPSO,the charactristic of the irregular motion of fractal Brownian motion model is used to simulate the optimization process varying in unknown mode,and its implied trend part is applied to simulate the optimization index of the global objective function optimum change. Therefore,the individual evolution process is prevented from going too randomly and precociously. Compared with the classic PSO algorithm,a fractal evolutionary phase is included for each particle in FEPSO. In this phase,each particle simulates a fractal Brownian motion with different Hurst parameter to search the solution in sub dimensional space,and its corresponding sub position is updated. The results of simulation experiments show that the proposed algorithm has a robust global search ability for most standard composite test functions and its optimization ability performs better than the recently proposed improved algorithm based on PSO.
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2013, Vol. 26 
