Abstract:According to the shortcomings of quantum behaved particle swarm optimization algorithm (QPSO), for instance, the lack of population diversity and getting trapped in local optima easily during the later stage of iteration, an improved algorithm based on cross operation is proposed. In the improved algorithm, particle’s history best position and suboptimal position are considered to expand its search space. Moreover, cross operation in genetic algorithm is used to renew particle’s position for enhancing population diversity and algorithm’s convergence. Through performance test, the improved algorithm is compared with the original quantum behaved particle swarm optimization algorithm, QPSO with differential evolution and QPSO based on black hole exploration in convergence accuracy and robustness. Finally, the improved algorithm is used to solve a kind of portfolio problems with quantity constraints, and the related optimization results are compared with genetic algorithm, particle swarm optimization algorithm and the standard quantum behaved particle swarm optimization algorithm.