摘要
针对公交车辆调度的运行环境以及其现状,考虑到信号灯周期对乘客等车时间的影响,同时为了保证公交公司与乘客的两者利益,建立了公交车辆优化调度模型。针对拒绝策略容易产生效率低的问题,采用惩罚策略设计出一种新的适应度函数。基于基本遗传算法存在早熟收敛和易陷入局部最优解等问题,本文采用量子遗传算法来解决组合问题。研究结果表明,该方法能够有效地解决公交车辆运营优化调度的组合问题。
According to operation environment and the present situation in the scheduling of public traffic vehicle,signal lamp cycle is taken into account the effect of waiting time of passengers in this paper. A bus optimization scheduling model is built,and it can ensure bus company＇s economic and passengers＇ benefits at the same time. According to the problem of low efficiency by the refused strategy,a new fitness function is designed by the penalty strategy. Based on the problems of the simple genetic algorithm in premature convergence and easily getting into local optimum and so on,combining quantum computation with genetic algorithm,quantum genetic algorithm which increases the convergence velocity and good global search capacity is put forward in this paper. Research result shows that the algorithm can effectively solve the combining problems of the bus optimization scheduling.
出处
《实验室研究与探索》
CAS
北大核心
2014年第12期72-76,共5页
Research and Exploration In Laboratory
基金
国家自然科学基金资助(61306007)
河南省教育厅科学技术研究重点项目(14B120003)
河南省科技攻关项目(122102210403)
重庆市自然科学基金重点项目(CSTC2012JJ40002)
南阳师范学院科研项目(ZX2014085)
关键词
公共交通
遗传算法
量子计算
信号灯周期
发车间隔
public transport
genetic algorithm
quantum computing
signal lamp cycle
departure intervals
作者简介
崔明月（1974-），男，河南商丘人，博士，讲师．主要研究方向：智能交通与智能控制，机器人控制。Tel．：18338292246；E—mail：cuiminyue@sina．com
