Stochastic Dynamic Step Length Particle Swarm Optimization

Abstract

Stochastic particle swarm optimization is a novel variant of particle swarm optimization that convergent to the global optimum with probability one. However, the local search capability is not always well in some cases, therefore, in this paper, a technique, dynamic step length, is incorporated into the structure of stochastic particle swarm optimization aiming to further improve the performance. In this modification, each particle will adjust its velocity according to its performance. In other words, if it finds a better region, it will make a local search, otherwise, a global search pattern is given. By the way, to combining the advantages between the standard version (with better exploitation capability) and the stochastic version (with better exploration capability), the first half period is used with the standard version incorporated with dynamic step length, while in later generations, the stochastic version with dynamic step length is used to escape from a local optimum. Simulation results show this strategy may provide well balance between exploration and exploitation capabilities, and improve the performance significantly.