Hybrid quantum particle swarm optimization algorithm and its application

作     者：Yukun WANG Xuebo CHEN Yukun WANG;Xuebo CHEN

作者机构：School of Chemical EngineeringUniversity of Science and Technology Liaoning School of Electronic and Information EngineeringUniversity of Science and Technology Liaoning 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2020年第63卷第5期

页      面：203-205页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 71571091  71771112  61473054) 

主　　题：Hybrid quantum particle swarm optimization algorithm and its application 

摘      要：Dear editor,Quantum-behaved particle swarm optimization(QPSO) is an evolutionary algorithm with quantum behavior. It can be used to solve optimization problems by establishing a potential well at the local attraction point to influence the location of particles [1, 2]. The algorithm offers many advantages, such as the requirement of a few parameters,simple operation, and strong convergence ability.