A New Particle Swarm Optimization with Quadratic Crossover

Abstract

In this paper we have presented a new variant of diversity guided PSO algorithm named QIPSO for solving global optimization problems. The QIPSO algorithm makes use of a quadratic crossover operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing premature convergence. We have compared it with Basic Particle Swarm Optimization (BPSO) and another diversity guided PSO called ARPSO. The numerical results show that QIPSO outperforms the other two algorithms in all the seventeen cases taken in this study.