A sequential niching technique for particle swarm optimization

Abstract

This paper proposed a modified algorithm, sequential niching particle swarm optimization (SNPSO), for the attempt to get multiple maxima of multimodal function. Based on the sequential niching technique, our proposed SNPSO algorithm can divide a whole swarm into several sub-swarms, which can detect possible optimal solutions in multimodal problems sequentially. Moreover, for the purpose of determining sub-swarm's launch criteria, we adopted a new PSO space convergence rate (SCR), in which each sub-swarm can search possible local optimal solution recurrently until the iteration criteria is reached. Meanwhile, in order to encourage every sub-swarm flying to a new place in search space, the algorithm modified the raw fitness function of the new launched sub-swarm. Finally, the experimental results show that the SNPSO algorithm is more effective and efficient than the SNGA algorithm. © Springer-Verlag Berlin Heidelberg 2005.