摘要
量子遗传算法(QGA)是将经典的量子理论应用到遗传算法当中,将量子态引入传统比特模型中,一种新型的求解最优问题的算法;越库配送车辆调度是一类经典的组合优化问题,基于量子遗传算法,针对提高物流配送过程中要求的快速和高效的问题,研究了一种混合量子遗传算法的框架,提出了解决传统物流调度中的配送优化方案的新思路,研究了新的量子更新和概率调整的策略,使该方法更加贴合物流配送的实际问题,实验结果显示,采用混合量子遗传算法后的性能明显优于传统的量子遗传算法,取得了更高的最佳适应度,具有良好的应用前景。
Quantum genetic algorithm is a new kind of algorithm to solve the optimization problem,which combine the classical quantum algorithm with the genetic algorithm and apply the quantum state to the Bit Model.Vehicle scheduling problem is a classic combinatorial optimization problem.This paper is mainly about a new Hybrid Quantum Genetic Algorithm framework to increase the speed and improve the efficiency of vehicle scheduling,which is more relevant to real problems.The result shows that the Hybrid Quantum Genetic Algorithm gets a higher fitness level than traditional way.It proves that the performance of improved method is better and has a good application prospect.
作者
杨玥
白士宇
殷雪峰
Yang Yue;Bai Shiyu;Yin Xuefeng(Liaoning Provincial Key Laboratory of Information Physics Fusion and Intelligent Manufacturing for Grade CNC Machine,Shenyang Institute of Technology,Fushun 113122,China;College of Mechanical and Vehicle Engineering,Shenyang Institute of Technology,Fushun 113122,China)
出处
《计算机测量与控制》
2019年第4期208-212,共5页
Computer Measurement &Control
基金
沈阳工学院校级基金(i5201801)
关键词
车辆调度
组合优化
混合量子遗传算法
vehicle scheduling
combinatorial optimization
hybrid quantum genetic algorithm
