关键词: 粒子群优化,一般性反向学习,自适应柯西变异,自适应惯性权值。

Abstract: To solve the problem of premature convergence in traditional particle swarm optimization (PSO),this paper proposed a opposition-based particle swarm optimization with adaptive Cauchy mutation.The new algorithm applies adaptive Cauchy mutation strategy (ACM) on the basis of generalized opposition-based learning method (GOBL).GOBL strategy to generate solutions can expand the search space and enhance the global explorative ability of PSO.Meanwhile,adaptive Cauchy mutation strategy was presented to disturb the current optimal particle and adaptively gain variation points in order to avoid the best particle being trapped into local optima,since this may cause search stagnation.This strategy is helpful to improve the exploitation ability of PSO and make the algorithm more smoothly fast converge to the global optimal solution.In order to further balance the global search and local explorative ability of the algorithm,this paper applied a nonlinear adaptive inertia weight.The new algorithm was compared with several opposition-based PSO on 14 benchmark functions.The experimental results show that the new algorithm greatly improves accuracy and convergence speed of solution.

Key words: Particle swarm optimization,Generalized opposition-based learning,Adaptive Cauchy mutation,Adaptive inertia weigh