改进的蚁群算法在定制公交路径规划中的应用

Application of Improved Ant Colony Algorithm in Customized Bus Path Planning

针对传统蚁群算法规划的路径会出现收敛速度慢且容易陷入局部最优等问题,提出了一种基于蚁群算法改进的综合路径规划算法,并应用到定制公交问题中。首先,改进双向搜索策略,对概率转移做出改进,增大前期搜索范围,提升全局搜索能力;引入狼群分配策略,改变信息素更新规则,增强算法收敛性能;另外,以公交运营成本和乘客上座率作为优化目标,车辆核载人数、乘客预定时间为约束条件,构建综合评估模型,从而使规划出的路线更好的符合乘客和运营公司的利益;最后,通过改进的粒子群算法进行参数优化,求解符合本问题的最优参数,提升算法整体性能。仿真实验结果表明,改进后的蚁群算法规划的路径更加合理,能有效避免局部最优问题,求解时间更短。

Aiming at the problems of slow convergence and easy to fall into the local optimum for paths planned by traditional ant colony algorithm,an improved comprehensive path planning algorithm based on ant colony algorithm is proposed and applied to the customized bus problem.Firstly,the two-way search strategy and the transition probability formula are improved,the scope of the previous search is increased,and the global search ability is improved.The wolf pack allocation strategy is introduced,the pheromone update rules are changed,and convergence performance of the algorithm is enhanced.In addition,taking the operation cost of public transport and the passenger occupancy rate as the optimization objective,and the number of passengers and the scheduled time of passengers as the constraints,a comprehensive evaluation model is constructed.So that the planned route is better in line with the interests of passengers and operating companies.Finally,the parameters of the ant colony algorithm are optimized by an improved particle swarm algorithm,and the optimal parameters that meet this problem are solved to improve the overall performance of the algorithm.Simulation results show that the path planned by the improved ant colony algorithm is more reasonable,can effectively avoid local optimization problems,and has a shorter solution time.