A Novel Binary Quantum-behaved Particle Swarm Optimization Algorithm

摘要：

　　To keep the balance between the global search and local search,a novel binary quantum-behaved particle swarm optimization algorithm with comprehensive learning and cooperative approach(CCBQPSO)is presented.In the proposed algorithm,all the particles personal best position can participate in updating the local attractor firstly.Then all the particles previous personal best position and swarms global best position are performed in each dimension of the solution vector.Five test functions are used to test the performance of CCBQPSO.The results of experiment show that the proposed technique can increase diversity of swarm and converge more rapidly than other binary algorithms.

关键词： quantum-behaved particle swarm optimization binary comprehensive cooperative

作者: Jing Zhao Ming Li Zhihong Wang Wenbo Xu

作者单位: School of Information,Qilu University of Technology,Jinan,China College of Science and Technology,Shandong University of Traditional Chinese Medicine,Jinan,China Shandong Yingcai University,Jinan,China School of Internet of Things Engineering,Jiangnan University,Wuxi,China

会议类型: 国际会议

会议名称: The 12th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2013)(第十二届分布式计算及其应用国际学术研讨会)

会议地点: 英国伦敦

会议语种:英文

页码: 119-123

在线出版日期: 2013-09-02（万方平台首次上网日期，不代表论文的发表时间）