EFFECTIVENESS OF SECOND BEST PARTICLE INFORMATION FOR PARTICLE SWARM OPTIMIZATION

Abstract

Particle Swarm Optimization (PSO) represents the potential solutions of the optimization problem as the particles and then, the particles move in order to find the better solution. The particle positions are updated from the personal best and the global best particle positions which have been ever found. This research focuses on the use of the second personal best and the second global best particle positions in order to improve the search performance of the original PSO algorithm. In the present algorithm, the second global best or the second personal best particle position is randomly used for updating all particle positions. The algorithms are compared with the original PSO algorithm in five test functions. The results reveal that the use of the second global best and the second personal best particle positions can improve the search performance of the original PSO although the basic idea is simple.