Improving PSO by Query-Based Learning

Motivation: PSO (particle swarm optimization) is one of the most important research topics on artificial intelligence. PSO still remain some disadvantages. This paper tries to discuss the disadvantages of PSO and to find a solution for improving its performance.

Method: We apply the query-based learning method proposed in our previous papers to PSO. It leads the particles to extend their search area. Thus, not only the precision of solution but also the time consumed is improved. We visualize the mechanism through a two-dimension PSO and verify the mechanism by several functions. Conventional PSO usually leads the particles go into the wrong direction of evolution. To resolve this drawback, when particles tend to converge, we spread some particles into ambiguous solution space. Furthermore, PSO has been well improved.

Achievement: This thesis, in our knowledge, is the first study that applies the QBL concept in Particle Swarm Optimization. The experiment results show the proposed approach is able to prevent the system from falling into local optimal and improve the performance of PSO.