A Novel Quantum-behaved Particle Swarm Optimization Algorithm

摘要：

　　A novel Quantum-behaved Particle Swarm Optimization algorithm with probability(P-QPSO)is introduced to improve the global convergence property of QPSO.In the proposed algorithm,all the particles keep the original evolution with large probability,and do not update the position of particles with small probability,and re-initialize the position of particles with small probability.Seven benchmark functions are used to test the performance of P-QPSO.The results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.

关键词： particle swarm optimization algorithm quantum-behaved probability benchmark function

作者: Jing Zhao Hong Liu

作者单位: School of Information,Qilu University of Technology;Shandong Provincial Key Laboratory for Distribut Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology,College of Inf

会议类型: 国际会议

会议名称: The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

会议地点: 贵阳

会议语种:英文

页码: 94-97

在线出版日期: 2015-08-18（万方平台首次上网日期，不代表论文的发表时间）