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

被引：0
|
作者：
Su, Jinghua [1 ]
Xu, Li [2 ]
机构：
[1] Dalian Jiaotong Univ, Inst Software, Dalian, Peoples R China
[2] Dalian Jiaotong Univ, Inst Mech Engn, Dalian, Peoples R China
来源：
PROCEEDINGS OF 2019 IEEE 7TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2019) | 2019年
关键词：
Quantum genetic algorithm; Cloud model; style; Flexible job shop scheduling;
D O I：
10.1109/iccsnt47585.2019.8962476
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The quantum genetic algorithm is improved for the flexible shop scheduling problem. In this paper, 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.
引用
收藏
页码：1 / 5
页数：5
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