[1]毕晓君,刘国安,肖婧.基于自适应差分进化的干线交通信号协调控制[J].智能系统学报,2012,7(5):437-443.
　BI Xiaojun,LIU Guoan,XIAO Jing.Coordination and control of arterial traffic signalsbased on adaptive differential evolution[J].CAAI Transactions on Intelligent Systems,2012,7(5):437-443.
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基于自适应差分进化的干线交通信号协调控制

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 7 期数: 2012年第5期 页码: 437-443 栏目: 学术论文—智能系统 出版日期: 2012-10-25
Title:
Coordination and control of arterial traffic signalsbased on adaptive differential evolution
文章编号:
1673-4785(2012)05-0437-07
作者:
毕晓君1，刘国安1，肖婧2
1.哈尔滨工程大学 信息与通信工程学院，黑龙江 哈尔滨 150001；
2.辽宁省交通高等专科学校 信息工程系，辽宁 沈阳 110122
Author(s):
BI Xiaojun1, LIU Guoan1, XIAO Jing2
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. Department of Information Engineering, Liaoning Provincial College of Communications, Shenyang 110122, China
关键词:
差分进化; 智能交通; 干线协调控制; pADE; 多种群免疫算法
Keywords:
differential evolution;  intelligent transportation;  arterial coordination and control;  pbestbased adaptive differential evolution (pADE);  multipopulation immunity algorithm
分类号:
TP18
文献标志码:
A
摘要:
为克服现有基于传统智能优化算法的城市干线交通信号协调控制方法求解精度低、易陷入局部最优等缺陷，将改进后的动态自适应差分进化算法pADE应用于城市干线双向交通信号的协调优化控制，通过优化干线交叉路口相位差减小交通流平均延误.pADE在标准差分进化算法基础上提出了新变异策略和参数动态自适应调整策略，有效平衡算法的局部搜索与全局搜索能力.通过与基于多种群免疫算法等协调优化控制方法对比，实验结果表明，pADE在收敛精度、速度和鲁棒性上相比较于多种先进智能优化算法均具有明显优势，可以为交通干线系统提供更优的相位差，有效减少干线直行交通流的平均延误，提高城市主干道交通通行能力.
Abstract:
In order to improve the efficiency and stability of the coordination and control on urban arterial traffic signal of traditional intelligent optimization algorithms, and to avoid flaws such as low precision solution and local optima, a dynamic selfadaptive differential evolution algorithm, named pADE, will be examined. In the pADE, a new mutation strategy and a selfadaptive parameter adjustment technique are proposed to balance local and global searches for the improvement on convergence accuracy and speed. Experimental outcome illustrates the pADE algorithm outperformed several stateoftheart optimization algorithms. In addition, innovative coordination control methods are capable of offering better traffic signal patterns that can reduce the average delay of traffic flow. This new technology will ultimately help improve the traffic capacity of urban trunk road traffic.
参考文献/References:
［1］李晓红.城市干线交通信号协调优化控制及仿真［D］.大连:大连理工大学, 2007: 2247. 
LI Xiaohong. Urban arterial traffic signal coordination control and simulation［D］. Dalian: Institute of Technology of Dalian, 2007: 2247.
［2］陈小锋.城市交通信号动态优化控制技术研究［D］.西安:西北工业大学, 2003: 2556. 
CHEN Xiaofeng. Research on dynamic optimization and control techniques for urban traffic signal［D］. Xi＇an:Northwestern Polytechnical University, 2003: 2556.
［3］王东.城市干线交通信号协调控制研究［D］.西安:长安大学, 2010: 2866. 
WANG Dong. The study of urban arterial traffic signal coordination control based on genetic algorithm［D］. Xi＇an: Chang＇an University, 2010: 2866.
［4］沈国红.城市道路交通智能控制技术研究［D］.杭州:浙江大学, 2004: 3358. 
SHEN Guohong. A study on intelligent control technique for urban traffic［D］. Hangzhou: Zhejiang University, 2004: 3358.
［5］朱晓燕.城市干线交通流仿真研究及软件设计［D］.南京:东南大学, 2005: 2568. 
ZHU Xiaoyan. Simulation research and software design of urban trunk road traffic flow［D］. Nanjing: Southeast University, 2005: 2568.
［6］徐建伟.基于免疫算法的城市干线交通信号协调控制研究［D］.湘潭:湘潭大学, 2008: 2186. 
XU Jianwei. Urban arterial traffic signal coordination control research based on immune algorithm［D］. Xiangtan: Xiangtan University, 2008: 2186.
［7］顾榕,曹立明,王小平.基于改进免疫遗传算法的交通信号优化控制［J］.模式识别与人工智能, 2006, 19(3): 331337. 
GU Rong, CAO Liming, WANG Xiaoping. The optimization for traffic signal based on improved immune genetic algorithm［J］. Pattern Recognition and Artificial Intelligence, 2006, 19(3): 331337.
［8］李乐,史忠科.基于遗传算法改进的交通干线信号优化研究［J］.计算机仿真, 2009, 26(1): 260263. 
LI Le, SHI Zhongke. Traffic trunk road signal optimization based on an improved genetic algorithm［J］. Computer Simulation, 2009, 26(1): 260263.
［9］付绍昌,黄辉先,肖业伟,等.自适应变异粒子群算法在交通控制中的应用［J］.系统仿真学报, 2007, 19(7): 15621564, 1612. 
FU Shaochang, HUANG Huixian, XIAO Yewei, et al. Application of adaptive mutationparticle swarm optimization algorithm in traffic control［J］. Journal of System Simulation, 2007, 19(7): 15621564, 1612.
［10］PRICE K, STORN R M, LAMPINEN J A. Differential evolution: a practical approach to global optimization［M］. Berlin, Germany: SpringerVerlag, 2005.
［11］吴亮红.差分进化算法及应用研究［D］.长沙:湖南大学, 2007: 2489.
WU Lianghong. The research and application of differential evolution algorithm［D］. Changsha: Hunan University, 2007: 2489.
［12］ZAHARIE D. Critical values for the control parameters of differential evolution algorithms［C］//Proceedings of the 8th International Conference on Soft Computing. Brno, Czech Republic, 2002: 6267.
［13］LAMPINEN J, ZELINKA I. On stagnation of the differential evolution algorithm［C］//Proceedings of the 6th International Conference on Soft Computing. Brno, Czech Republic, 2000: 7683.
［14］FAN Huiyuan, LAMPINEN J. A trigonometric mutation operation to differential evolution［J］. Journal of Global Optimization, 2003, 27(1): 105129.
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备注/Memo

收稿日期： 2012-07-18.
网络出版日期：2012-09-24.
基金项目：国家自然科学基金资助项目(61175126);教育部博士点基金资助项目(20112304110009)；中央高校基本科研业务费重大项目培育计划资助项目(HEUCFZ1209). 
通信作者：刘国安.
E-mail: liuguoan@hrbeu.edu.cn.
作者简介：
毕晓君，女，1964年生，教授，博士生导师，博士.主要研究方向为智能信息处理、智能优化算法理论及应用.先后承担“十一?五”预研项目、国家自然科学基金项目以及省部级科研项目12项，曾获省部级科学技术进步二等奖3项、三等奖4项.发表学术论文51篇，其中被EI检索23篇、ISTP检索5篇，出版专著3部，获国家发明专利授权1项. 
刘国安，男，1983年生，助理研究员，博士研究生，主要研究方向为智能优化算法理论及应用.参与国家自然科学基金项目1项，发表学术论文5篇，其中被EI检索4篇. 
肖婧，女，1985年生，讲师，博士，主要研究方向为智能信息处理、智能优化算法理论及应用.承担辽宁省教育厅项目1项、辽宁省科技厅博士科研启动基金项目1项，发表学术论文7篇，其中被SCI检索2篇、EI检索4篇.

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