%0 Journal Article
%T Multi-robot mission assignment based on current learning discrete particle swarm optimization algorithm<br>基于当代学习离散粒子群算法的多机器人任务分配*
%A YU Ling-li
%A CAI Zi-xing
%A <br>余伶俐
%A 蔡自兴
%J 计算机应用研究
%D 2009
%I 
%X Multi-robot mission assignment mathematical model was established firstly, which considered three factors comprehensively: executing mission efficiency, robot ability and mission properties. This paper proposed current learning discrete particle swarm optimization algorithm(CLDPSO) to solve multi-robot mission assignment with highly efficiently. The algorithm designed an exact particles kinetic equation. When decreased algorithm diversity to a certain threshold,added a perturbation operator to jump out local optimum quickly and to improve the search ability. The experiment results show that CLDPSO can reach the best result, and its stability is the best among existing algorithms when the number of missions is small scale. When the number of missions is middle or large scale, the searching optimization ability is also strong. Those experiments prove that the model is reasonably and CLDPSO algorithm is the advantage.
%K multi-robot
%K current learning discrete particle swarm
%K mission assignment
%K perturbation factor<br>多机器人
%K 离散粒子群
%K 任务分配
%K 扰动因子
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=23C536D6DFC79D0EC5ABA9E484C5D7CC&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=94C357A881DFC066&sid=34603A9A580CC7B9&eid=B9018D9DCA7DD012&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=2&reference_num=21