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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 [1]. The numerical results show that QIPSO outperforms the other two algorithms in all the seventeen cases taken in this study.