融合模拟退火的遗传算法在车辆调度中的应用  

Application of Genetic Algorithm Integrated with Simulated Annealing in Vehicle Dispatching Problem

作　　者：刘睿琼[1] 张文丽[2] 侯爱华[1] LIU Ruiqiong;ZHANG Wenli;HOU Aihua(High-Tech College,Xi'an University of Technology,Xi'an 710082;School of Physics and Telecommunication Engineering,Shaanxi University of Technology,Hanzhong 723000)

机构地区：[1]西安理工大学高等技术学院,西安710082 [2]陕西理工大学物电学院,汉中723000

出　　处：《计算机与数字工程》2018年第7期1316-1319,1400,共5页Computer & Digital Engineering

基　　金：国家自然科学青年基金项目(编号:61401262)资助

摘　　要：论文在遗传算法搜索过程中融入了模拟退火算法,针对选择运算、交叉运算和变异运算产生的新种群,使用模拟退货算法逐一进行优化。由于模拟退火算法不仅接收使目标函数变好的解,还在一定程度上接收使目标函数变差的解,有效避免陷入局部最优,克服了遗传算法局部搜索能力较差、易出现早熟现象的缺点,提高了遗传算法的性能,扩大了遗传算法的使用范围。仿真结果表明融合了模拟退火思想的改进遗传算法性能更优更稳定。In this paper,the simulated annealing algorithm is integrated into the search process of genetic algorithm.The new species group,which is generated by selection operation,crossover operation and mutation operation,is optimized by using the simulated annealing algorithm.The simulated annealing algorithm not only receives the solution making objective function better,but to some extent receives the solution making objective function worse.It can effectively avoid the local optimum,and can overcome the defects that genetic algorithm has poor local search ability and is prone to premature.It improves the performance of genetic algorithm and expands the scope of the use of genetic algorithm.The simulation results show that the improved genetic algorithm,which combines the simulated annealing thought,is better and more stable.

关 键 词：改进遗传算法 模拟退火思想 融合模拟退火的遗传算法 车辆调度 时间约束 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]