Comparative Particle Swarm Optimization (CPSO) for solving optimization problems

Particle Swarm Optimization (PSO) is a stochastic and population-based intelligence search algorithm, which has been demonstrated to solve optimization problem effectively. However, as the particle properties become increasingly similar after several generations, the particles tend to cluster around the best (fittest) particle in the swarm, which results in premature convergence of the PSO algorithm. In other words, the particles get trapped in the local optimal solution. In this paper, a new conception of PSO is proposed, which is based on comparing the experience of all particles in the swarm to generate a better position, and guide all particles toward the best possible solution. Experiments conducted on three benchmark functions show that the new algorithm is more efficient than standard PSO.