云自适应量子遗传算法求解柔性作业车间调度问题  

Cloud-based Adaptive Quantum Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem

作　　者：张鹏[1] 闫开濮 ZHANG Peng;YAN Kaipu(Innovation Entrepreneurship Institute of Education,Dalian Jiaotong University,Dalian 116028,China;Institute of Software,Dalian Jiaotong University,Dalian 116045,China)

机构地区：[1]大连交通大学创新创业教育学院,辽宁大连116021 [2]大连交通大学软件学院,辽宁大连116054

出　　处：《大连交通大学学报》2019年第6期111-116,共6页Journal of Dalian Jiaotong University

基　　金：中国博士后科学基金资助项目(2017M611231);辽宁省自然科学基金资助项目(20180550499)

摘　　要：针对柔性作业车间调度问题的动态性和不确定性特点,提出一种基于云计算和量子理论的遗传算法.设计自适应调整旋转角的量子遗传算法来获取初始解,利用云模型的随机性和稳定倾向性来自适应调整旋转角,从而提高获取解的质量和获取最优解的效率.借鉴物理学的量子交叉和量子变异特性来保证种群的多样性,以克服早熟收敛的缺陷.通过数值实验表明,提出的算法拥有良好的性能,可以获取较理想的解.The quantum genetic algorithm is improved for the flexible shop scheduling problem,and a quantum genetic algorithm adaptively adjusting the rotation angle is designed to solve the flexible shop scheduling problem.The randomness and stability tendency of the cloud model are used to adaptively adjust the rotation angle to improve the optimization ability of the algorithm.At the same time,quantum crossover and quantum variability are used to improve population diversity and prevent premature convergence.Numerical experiments show that the algorithm has good performance.

关 键 词：量子遗传算法 云模型 柔性作业车间调度 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]