Effect of Second Best Particle Information for Particle Swarm Optimization

In particle swarm optimization (PSO), the particle position vectors denotes the potential solutions of the problem. The position vectors are updated from the information of the global best particle and the personal best particles. When all particles gather to one position, the search process does not evolve any more. For overcoming this diffculty, this paper focuses on the use of the second best particle information. The present algorithm uses second global best or second personal best particles in addition to first global best and first personal best particles. The present algorithms are compared with the original PSO algorithm in the solution problem of test functions. The results show that the use of the second best particles can improve the search performance of the original PSO algorithm.