Particle Swarm Optimization with adaptive polynomial mutation

Particle Swarm Optimization (PSO) has shown its good search ability in many optimization problem. But PSO easily gets trapped into local optima while dealing with complex problems. In this work, we proposed an improved PSO, namely PSO-APM, in which adaptive polynomial mutation strategy is employed on global best particle with the hope that it will help the particles jump out local optima. In this work, we carried out our experiments on 8 well-known benchmark problems. Finally the results are compared with classical PSO and PSO with power mutation (PMPSO).