一种自适应柯西变异的反向学习粒子群优化算法

针对传统粒子群优化算法易出现早熟的问题,提出了一种自适应变异的反向学习粒子群优化算法。该算法在一般性反向学习方法的基础上,提出了自适应柯西变异策略(ACM)。采用一般性反向学习策略生成反向解,可扩大搜索空间,增强算法的全局勘探能力。为避免粒子陷入局部最优解而导致搜索停滞现象的发生,采用ACM策略对当前最优粒子进行扰动,自适应地获取变异点,在有效提高算法局部开采能力的同时,使算法能更加平稳快速地收敛到全局最优解。为进一步平衡算法的全局搜索与局部探测能力,采用非线性的自适应惯性权值。将算法在14个测试函数上与多种基于反向学习策略的PSO算法进行对比,实验结果表明提出的算法在解的精度以及收敛速度上得到了大幅度的提高。

Opposition-based Particle Swarm Optimization with Adaptive Cauchy Mutation

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.