%0 Journal Article
%T Hybrid particle swarm optimization for Job-Shop scheduling<br>一种求解作业车间调度的混合粒子群算法*
%A TANG Hai-bo
%A YE Chun-ming
%A <br>唐海波
%A 叶春明
%J 计算机应用研究
%D 2011
%I 
%X A new hybrid algorithm is introduced into solving job shop scheduling problems, which combines knowledge evolution algorithm(KEA) and particle swarm optimization(PSO) algorithm. By the mechanism of KEA, its global search ability is fully utilized for finding the global solution. By the operating characteristic of PSO, the local search ability is also made full use. Through the combination, better convergence property is obtained for job shop scheduling with the criterion of minimization the maximum completion time (makespan). Simulation results based on well-known benchmarks and comparisons with standard genetic algorithm demonstrate the feasibility and effectiveness of the proposed hybrid algorithm.
%K job shop scheduling
%K knowledge evolution algorithms
%K particle swarm optimization<br>作业车间调度
%K 知识进化算法
%K 粒子群优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=0C4AE32DA591F07706187140110A4AA7&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=38B194292C032A66&sid=899CC9158FC43EF4&eid=745C7FAEA69986C7&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=12