《计算机应用研究》|Application Research of Computers

量子鲸鱼优化算法求解作业车间调度问题

Solving Job-Shop scheduling problem by quantum whale optimization algorithm

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作者 闫旭,叶春明,姚远远
机构 上海理工大学 管理学院,上海 200093
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文章编号 1001-3695(2019)04-004-0975-05
DOI 10.19734/j.issn.1001-3695.2017.10.0985
摘要 为了克服基本鲸鱼优化算法(WOA)在解决作业车间调度问题时存在收敛精度低、容易陷入局部最优的缺陷,利用量子计算与优化思想提出了一种量子鲸鱼优化算法(QWOA),并对其进行了计算复杂度分析、全局收敛性证明及仿真实验。通过对11个作业车间调度问题基准算例的仿真实验发现,与基本鲸鱼优化算法、布谷鸟搜索算法(CS)、灰狼优化算法(GWO)相比,QWOA算法在最小值、平均值、寻优成功率等方面具有较优结果。研究表明,量子鲸鱼优化算法在解决作业车间调度问题时,具有更高的收敛精度和更好的全局搜索能力,且能够跳出局部最优。
关键词 鲸鱼优化算法; 量子计算与优化; 作业车间调度; 收敛性证明; 混合算法
基金项目 国家自然科学基金资助项目(71271138)
上海理工大学科技发展项目(16KJFZ028)
上海市高原学科项目(GYXK1201)
本文URL http://www.arocmag.com/article/01-2019-04-004.html
英文标题 Solving Job-Shop scheduling problem by quantum whale optimization algorithm
作者英文名 Yan Xu, Ye Chunming, Yao Yuanyuan
机构英文名 Business School,University of Shanghai for Science & Technology,Shanghai 200093,China
英文摘要 To overcome whale optimization algorithm's disadvantages of poor convergence and being easily trapped in local optima in solving Job-Shop scheduling problem, this paper proposed a quantum whale optimization algorithm(QWOA) based on the idea of quantum computation. Then it provided the computational complexity analysis, global convergence proof and simulation experiments of QWOA. Based on a set of 11 JSP benchmark instances, the simulation experiments show that QWOA has better results than whale optimization algorithm(WOA), cuckoo search(CS) and grey wolf optimizer(GWO), in the minimum value, average value and success rate. Finally, this paper concludes that QWOA has the merits of higher convergence accuracy, higher local optima avoidance and better exploration.
英文关键词 whale optimization algorithm; quantum computing and optimization; Job-Shop scheduling problem; convergence proof; hybrid algorithm
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收稿日期 2017/10/27
修回日期 2017/12/6
页码 975-979
中图分类号 TP301.6
文献标志码 A