A Local Best Particle Swarm Optimization Based on Crown Jewel Defense Strategy

Particle swarm optimization (PSO) is a swarm intelligence algorithm well known for its simplicity and high efficiency on various optimization problems. Conventional PSO suffers from premature convergence due to the rapid convergence speed and lack of population diversity. PSO is easy to get trapped in local optimal, which largely deteriorates its performance. It is natural to detect stagnation during the optimization, and reactivate the swarm to search towards the global optimum. In this work the authors impose the reflecting bound-handling scheme and von Neumann topology on PSO to increase the population diversity. A novel Crown Jewel Defense (CJD) strategy is also introduced to restart the swarm when it is trapped in a local optimal. The resultant algorithm named LCJDPSO-rfl is tested on a group of unimodal and multimodal benchmark functions with rotation and shifting, and compared with other state-of-the-art PSO variants. The experimental results demonstrate stability and efficiency of LCJDPSO-rfl on most of the functions.