摘要
针对粒子群优化算法搜索空间有限、容易出现早熟现象的缺陷,将量子粒子群优化算法用于求解车间调度问题,按照量子粒子群优化算法的进化规则在调度空间内搜索最优解,并对量子粒子群算法的参数选择进行了研究。以典型的Job-Shop调度问题作为实验对象,实验结果表明QPSO算法相对PSO算法具有较好的全局搜索能力。
Dealing with the disadvantages of PSO algorithm that finite sampling space, being easy to run to prematurity, the quan- tum particle swarm optimization （QPSO） algorithm is proposed to be applied to solve the job-shop scheduling problem. According to the evolution formulation of QPSO algorithm, it is the best optimization way to search in scheduling space. Likewise, the parameter se- lection of QPSO is studied. Finally, the two proposed algorithms are tested on some well-known scheduling problems. The simulation results show that the QPSO algorithm has better global search ability than that of PSO algorithm.
出处
《山西电子技术》
2013年第2期25-27,共3页
Shanxi Electronic Technology
关键词
车间调度
组合优化
量子粒子群优化算法
job-shop scheduling
combinatorial optimization
quantum particle swarm optimization
