模拟退火蚁群算法在最优路径选择中的应用  

Application of Simulated Annealing Ant Colony Algorithm in Optimal Path Selection

作　　者：张俊豪 ZHANG Jun-hao(Railway Police College,Zhengzhou,Henan 450003)

机构地区：[1]铁道警察学院图像与网络侦查系,河南郑州450003

出　　处：《怀化学院学报》2022年第5期68-75,共8页Journal of Huaihua University

基　　金：铁道警察学院基科费项目“人工智能技术在智慧交通中的应用研究”(2022TJJBKY028);河南省科技攻关项目“视频监控中前景目标检测关键技术研究”(212102210531)。

摘　　要：道路寻优是研究智慧城市以及解决道路交通拥堵的一个重要课题,为此提出一种基于模拟退火蚁群算法的最优交通路径选择算法.该算法首先根据实际交通路况改变蚁群算法的信息素初始分配机制,结合最短路径算法改变启发式函数,以此减小蚁群盲目搜索的概率;其次该算法加入“负反馈”机制,根据最优最差蚁群算法改进信息素更新机制,提升计算效率;最后根据模拟退火算法对路径进一步优化,以此解决蚁群算法易陷入局部最优及收敛速度较慢等问题.通过与其他几种经典的路径寻优算法对比分析,该算法在求解精度及求解效率上都有明显的改善.Road optimization is an important subject to study smart city and solve road traffic congestion. Therefore,an optimal traffic path selection algorithm based on simulated annealing ant colony algorithm is proposed. Firstly, the algorithm changes the pheromone initial allocation mechanism of ant colony algorithm according to the actual traffic conditions,and changes the heuristic function combined with the shortest path algorithm,so as to reduce the probability of blind search of ant colony. Secondly, the algorithm adds a“ negative feedback” mechanism, and improves the pheromone update mechanism according to the optimal worst ant colony algorithm to improve computing efficiency.Finally,the path is further optimized according to the simulated annealing algorithm,so as to solve the problem that the ant colony algorithm is easy to fall into local optimization and slow convergence speed. Through the comparative analysis of several other classical path optimization algorithms, the algorithm has significant improvement in solution accuracy and efficiency.

关 键 词：蚁群算法 模拟退火算法 最优路径 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]