蚁群算法与模糊控制在智能导航系统中的融合设计
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摘要
基于现代化城镇交通道路规划中的最优路径问题,构思了一种融合蚁群算法和模糊控制的智能导航系统设计方案.系统由前端数据采集单元、信息传输模块、服务器、车载终端显示装置组成,利用蚂蚁取食的方法来寻求最优路径,以最短的时间抵达目的地.仿真及试验结果表明:该系统可以通过模糊控制来简化系统设计,并结合蚁群算法有效缓解交通拥堵,具有较强的实用性和推广性.
Aiming at the optimal path problem in traffic road planning,this paper proposes a design scheme of intelligent navigation system based on fuzzy control and ant colony algorithm.The system consists of a front-end data acquisition unit,an information transmission module,a server,and a vehicle-mounted terminal display device.The principle of ant feeding is used to seek an optimal path and arrive at the destination in the shortest time.The simulation and experimental results show that the system can simplify the system design through fuzzy control and effectively alleviate traffic congestion by combining the ant colony algorithm,which has certain practicability and generalization.
Aiming at the optimal path problem in traffic road planning,this paper proposes a design scheme of intelligent navigation system based on fuzzy control and ant colony algorithm.The system consists of a front-end data acquisition unit,an information transmission module,a server,and a vehicle-mounted terminal display device.The principle of ant feeding is used to seek an optimal path and arrive at the destination in the shortest time.The simulation and experimental results show that the system can simplify the system design through fuzzy control and effectively alleviate traffic congestion by combining the ant colony algorithm,which has certain practicability and generalization.
引文
[1] YAO E J,LANG Z F,YANG Y.Vehicle Routing Problem Solution Considering Minimising Fuel Consumption[J].IEEE Transactions on Intelligent Transportation Systems,2015,9(5):523-529.
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[3] 王鹤,邵良杉,邱云飞.蚁群算法在城市公交线网优化中的应用[J].微计算机信息,2011,27(3):217-218,235.
[4] 王勇,刘国盼.基于蚁群算法的货物配送路径优化研究[J].内燃机与配件,2018(14):178-181.
[5] 宋志飞,张俊峰.基于改进蚁群算法的车辆路径优化[J].电脑知识与技术,2018,14(23):225-226,228.
[6] 宋方,汪镭.改进蚁群算法在智能交通中的应用[J].数学的实践与认识,2013,43(3):66-72.
[7] 金涛,李娜,王海军,等.基于二元蚁群算法的智能组卷的仿真设计研究[J].内蒙古农业大学学报(自然科学版),2015(2):137-142.
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[9] 马荣贵,崔华,薛世焦,等.改进蚁群算法的多约束质量最优路径选择[J].西安电子科技大学学报,2016,43(3):185-189.
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[13] CAO J.Robot global path planning based on an improved ant colony algorithm[J].Journal of Computer and Communications,2016,4(2):11-19.
[14] 宋杰,吴勇,陈明明.基于蚁群优化算法的无线传感器网络路由研究[J].赤峰学院学报(自然科学版),2015,31(9):13-15.
[15] 程凤林,张军芳,杜鹏,等.模糊控制方法介绍[J].山东工业技术,2019(6):149,167.
[16] 李庆中,顾伟康,叶秀清,等.移动机器人模糊控制方法研究[J].仪器仪表学报,2002(5):480-483,503.
[17] 李娟,游晓明,刘升,等.自适应模糊蚁群系统[J/OL].计算机工程与应用.(2019-04-18)[2019-04-25]http://kns.cnki.net/kcms/detail/11.2127.TP.20190417.1249.020.html.
[18] 董森,荣丽红,仝志民,等.基于模糊PID的机械臂控制系统设计[J].黑龙江八一农垦大学学报,2018,30(5):102-106.
[19] 周卫标,吴楚东,高敏.基于改进蚁群算法的配送路线优化分析[J].科技经济导刊,2018,26(15):10-11,19.
[20] 温惠英,徐建闽.基于改进型蚁群算法的车辆导航路径规划研究[J].公路交通科技,2009,26(1):125-129.
[21] 张成,凌有铸,陈孟元.改进蚁群算法求解移动机器人路径规划[J].电子测量与仪器学报,2016,30(11):1758-1764.
[2] LIN X,LO H K.Adaptive Vehicle Navigation with EN Route Stochastic Traffic Information[J].IEEE Transactions on Intelligent Transportation Systems,2014,15(5):1900-1912.
[3] 王鹤,邵良杉,邱云飞.蚁群算法在城市公交线网优化中的应用[J].微计算机信息,2011,27(3):217-218,235.
[4] 王勇,刘国盼.基于蚁群算法的货物配送路径优化研究[J].内燃机与配件,2018(14):178-181.
[5] 宋志飞,张俊峰.基于改进蚁群算法的车辆路径优化[J].电脑知识与技术,2018,14(23):225-226,228.
[6] 宋方,汪镭.改进蚁群算法在智能交通中的应用[J].数学的实践与认识,2013,43(3):66-72.
[7] 金涛,李娜,王海军,等.基于二元蚁群算法的智能组卷的仿真设计研究[J].内蒙古农业大学学报(自然科学版),2015(2):137-142.
[8] HUANG M,DING P.An Improved Ant Colony Algorithm and Its Application in Vehicle Routing Problem[J].Mathematical Problems in Engineering,2013:1-9.
[9] 马荣贵,崔华,薛世焦,等.改进蚁群算法的多约束质量最优路径选择[J].西安电子科技大学学报,2016,43(3):185-189.
[10] 葛斌,韩江洪,魏臻,等.最小最大车辆路径问题的动态自适应蚁群优化算法[J].模式识别与人工智能,2015,28(10):930-938.
[11] 许向阳,吴泽华.基于改进蚁群算法的消防路线规划系统[J].通信技术,2018,51(6):1358-1364.
[12] 郁磊,史峰,王辉,等.智能算法[M].北京:北京航空航天大学出版社,2015:9.
[13] CAO J.Robot global path planning based on an improved ant colony algorithm[J].Journal of Computer and Communications,2016,4(2):11-19.
[14] 宋杰,吴勇,陈明明.基于蚁群优化算法的无线传感器网络路由研究[J].赤峰学院学报(自然科学版),2015,31(9):13-15.
[15] 程凤林,张军芳,杜鹏,等.模糊控制方法介绍[J].山东工业技术,2019(6):149,167.
[16] 李庆中,顾伟康,叶秀清,等.移动机器人模糊控制方法研究[J].仪器仪表学报,2002(5):480-483,503.
[17] 李娟,游晓明,刘升,等.自适应模糊蚁群系统[J/OL].计算机工程与应用.(2019-04-18)[2019-04-25]http://kns.cnki.net/kcms/detail/11.2127.TP.20190417.1249.020.html.
[18] 董森,荣丽红,仝志民,等.基于模糊PID的机械臂控制系统设计[J].黑龙江八一农垦大学学报,2018,30(5):102-106.
[19] 周卫标,吴楚东,高敏.基于改进蚁群算法的配送路线优化分析[J].科技经济导刊,2018,26(15):10-11,19.
[20] 温惠英,徐建闽.基于改进型蚁群算法的车辆导航路径规划研究[J].公路交通科技,2009,26(1):125-129.
[21] 张成,凌有铸,陈孟元.改进蚁群算法求解移动机器人路径规划[J].电子测量与仪器学报,2016,30(11):1758-1764.