A modified niching particle swarm optimization algorithm for multimodal function
用于多峰函数优化的改进小生境微粒群算法

A modified niching Particle Swarm Optimization(PSO) algorithm was constructed which allowed unimodal function optimization methods to efficiently locate all optima of multimodal problems that the Niche PSO cannot reach.In the new algorithm,the sequential niche technique was introduced.Firstly,a stretching technique was adopted in main swarm.Secondly,the dismissal mechanism was used in subswarms namely when a local extreme point of value was found in sub-swarms,the sub-swarms would be dismissed and regressed to the main swarm.At last,the radius of created sub-swarms was confined in order to avoid the excessive of radius.The new Stretching-Niche PSO(SNPSO) algorithm could resolve the disadvantage of standard Niche PSO that the local best of value depends on the number of sub-swarms and easily has the problem of iteration and pretermission.Testing of the algorithm by using three benchmark functions indicate that the modified niching PSO has better performance than standard Niche PSO in terms of the stability,convergence and coverage in searching a better value.