Particle swarm optimization with generalized opposition based learning in particle's pbest position

摘要

This paper presents an improved Particle Swarm Optimizer with opposition based learning method. The key feature of this method is that opposition based learning scheme is employed in personal best position of particles called pbest position in order to improve the performance of particle swarm optimizer. The proposed method is termed as OpbestPSO. OpbestPSO is applied on 12 benchmark problems. The experimental results shows the better performance of the proposed method OpbestPSO.