Search performance improvement of Particle Swarm Optimization by second best particle information

In the original Particle Swarm Optimization (PSO), the particle position vectors denote the potential solutions of the optimization problem. Then, the position vectors are updated from the information of the global best and the personal best particles, which denote the best particle which has been ever found by all particles and the best particle which has been ever found by each particle, respectively. The aim of this study is to discuss that, in addition to the information of the global and personal best particles, the use of the information of the second global best and second personal best particles improves the search performance of the original PSO. Firstly, two algorithms are explained. One updates the particle positions by the positions of the global best, the personal best and second global best particles. Another uses second personal best particles instead of second global best particle. The present algorithms are compared with 6 PSO algorithms in 11 test functions. The results show that the present algorithms have the faster convergence speed and find better optimal solution than other algorithms. Therefore, it is concluded that the use of the second best particles can improve the search performance of the original PSO algorithm.