Momentum particle swarm optimization with optimal crossover
动量交叉粒子群算法

Aiming at the PSO's shortcoming about slow convergence rate and badly global searching ability, this paper presented a new particle swarm optimization with optimal crossoverOCPSO. By introducing a new simulated binary-crossover strategy SBX and a new strategy of inertia weight setting, it improved the ability of global and local searching. Furthermore, it utilized variable coefficient low-pass filters to update particles' positions of OCPSO, called momentum algorithm, which could enhance the speed and accuracy of convergence. Experimental results on several classical functions indicate that the new algorithm can greatly improve the searching speed and accuracy.