一种新的位置变异的PSO算法

针对标准粒子群优化算法在优化高维复杂函数时易产生早熟收敛的问题，提出一种新的位置变异的PSO算法。为平衡算法的全局和局部搜索能力，新算法按一定概率交替使用随机惯性权重和标准PSO算法的惯性权重；为增强种群多样性和抑制算法早熟，新算法在每次迭代中，对满足一定条件的粒子都进行一种有效脱离局部最优区域的位置变异。最后，通过对5个标准测试函数在60维和90维的性能对比实验证实：新算法收敛精度高，且有效克服了早熟收敛问题。

New particle swarm optimization algorithm with position mutation

Standard Particle Swarm Optimization（SPSO） easily leads to premature convergence in optimizing high-dimensional functions,to overcome this shortcoming,a New Particle Swarm Optimization algorithm with Position Mutation（NPSO-PM） is proposed.To balance the ability of local search and global search of PSO,NPSO-PM alternately uses random inertia weight and inertia weight of SPSO in possibility;to enhance the population diversity and to restrain premature convergence of PSO,the position of particles,which meet certain conditions,mutates in a method that can make particle escape from local areas effectively in each iteration.Finally,5 benchmark functions on 60 and 90 dimensions simulation experiments show that proposed algorithm has high convergence precision and overcomes premature convergence effectively.