To study the information processing method in QPSO（quantum-behaved particle swarm optimization） algorithm,random factors in the potential well center formula of QPSO was analyzed,the bivariate correlation factors concept was proposed,and internal relations between particles’own experience information (pbest) and group sharing information(gbest) using Normal Copula functions was established.Then,the binary correlations QPSO algorithm (BC-QPSO) was proposed,and the relations between the bivariate correlation factors and population diversity were studied through simulations. Simulation results of the six test functions showed that BC-QPSO algorithm outperfoms the standard QPSO algorithm in terms of optimization results by selecting the appropriate values of correlation coefficient.
