基于改进蚁群算法的智能交通路径规划
|
摘要
社会的快速发展,带来了越来越严重的交通问题,长期以来导致环境污染和能源浪费。专家提出智能交通系统能够有效地改善交通问题,而路径寻优算法又是其中的关键点之一,但是原来研究的算法往往只是针对路径长短,没有考虑实际的路况和当时的情景。该文结合传统蚁群算法,模拟现实的路况和情景改进算法,并进行仿真和数据分析。仿真实验结果显示,改进蚁群算法在动态路径规划中具有良好的效果。
The rapid development of society has brought more and more serious traffic problems. It has caused environmental pollution and wasted energy for a long time. Experts suggest that intelligent transportation systems can effectively improve traffic problems, and path optimization algorithms are one of the key points. However, the original research algorithm is often only for the length of the path, without considering the actual situation and the situation at that time. In this paper, the traditional ant colony algorithm is used to simulate the realistic road conditions and scene improvement algorithms, and simulation and data analysis are performed. The simulation results show that the improved ant colony algorithm has a good effect in dynamic path planning.
The rapid development of society has brought more and more serious traffic problems. It has caused environmental pollution and wasted energy for a long time. Experts suggest that intelligent transportation systems can effectively improve traffic problems, and path optimization algorithms are one of the key points. However, the original research algorithm is often only for the length of the path, without considering the actual situation and the situation at that time. In this paper, the traditional ant colony algorithm is used to simulate the realistic road conditions and scene improvement algorithms, and simulation and data analysis are performed. The simulation results show that the improved ant colony algorithm has a good effect in dynamic path planning.
引文
[1] 陆化普,李瑞敏.城市智能交通系统的发展现状与趋势[J].工程研究-跨学科视野中的工程,2014,6(01):6-19.
[2] 谢民,高利新.蚁群算法在最优路径规划中的应用[J].计算机工程与应用,2008(08):245-248.
[3] 申铉京,刘阳阳,黄永平,等.求解TSP问题的快速蚁群算法[J].吉林大学学报(工学版),2013,43(01): 147-151.
[4] 刘陪. 智能交通中动态路径诱导系统的建模与优化算法的研究[D].长春:吉林大学,2017.
[5] 李威武,王慧,钱积新.智能交通系统中路径诱导算法研究进展[J].浙江大学学报(工学版),2005(06):819-825.
[2] 谢民,高利新.蚁群算法在最优路径规划中的应用[J].计算机工程与应用,2008(08):245-248.
[3] 申铉京,刘阳阳,黄永平,等.求解TSP问题的快速蚁群算法[J].吉林大学学报(工学版),2013,43(01): 147-151.
[4] 刘陪. 智能交通中动态路径诱导系统的建模与优化算法的研究[D].长春:吉林大学,2017.
[5] 李威武,王慧,钱积新.智能交通系统中路径诱导算法研究进展[J].浙江大学学报(工学版),2005(06):819-825.