%0 Journal Article
%T Multi-peaks function optimization using quantum-behaved particle swarm optimization<br>一种求解多峰函数优化问题的量子行为粒子群算法
%A ZHAO Ji
%A SUN Jun
%A XU Wen-bo
%A <br>赵吉
%A 孙俊
%A 须文波
%J 计算机应用
%D 2006
%I 
%X An improved Quantum-behaved Particle Swarm Optimization (QPSO) for multi-peaks functions optimization was proposed. In the proposed Species-based QPSO (SQPSO), the swarm population was divided into subpopulations in parallel based on their similarity. Each subpopulation was set around a dominating particle called the species seed. Over successive iterations, species were able to simultaneously optimize towards multiple optima by using QPSO, so each peaks were ensure to be searched equally, no matter whether they are global or local optima. Experimental results demonstrate that SQPSO can search as many peaks of function as possible. Simulation results show the convergence performance of SQPSO is superior to that of PSO.
%K Quantum-behaved Particle Swarm Optimization(QPSO)
%K Particle Swarm Optimization(PSO)
%K species
%K multi-peak searching<br>量子行为粒子群算法
%K 粒子群算法
%K 物种形成策略
%K 多峰寻优
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=AEEE441D1D68601C&yid=37904DC365DD7266&vid=96C778EE049EE47D&iid=59906B3B2830C2C5&sid=A03EE4BDA6DE1854&eid=644ED03C44803849&journal_id=1001-9081&journal_name=计算机应用&referenced_num=1&reference_num=8