Applying Multi-Swarm Accelerating Particle Swarm Optimization to Dynamic Continuous Functions

In this paper, the particle swarm optimizer is modified to create the multi-swarm accelerating PSO which is applied to dynamic continuous functions. Different from the existing multi-swarm PSOs and local versions of PSO, the swarms are dynamic and the swarms' size is small. The whole population is divided into many small swarms, these swarms are regrouped frequently by using various regrouping schedules and information is exchanged among the swarms. Accelerating operators is combined to improve its local search ability. The MSA-PSO recognizes changes in the search space and adjusts to these changes in the environment. The effectiveness of the modification is demonstrated by application to some dynamic continuous functions.