基于改进粒子群算法的大件运输通行路径优化研究

Research on Route Optimization of Bulky Transportation Based on Improved Particle Swarm Optimization

为解决大件运输车辆申请起始地与目的地间的最优通行路径决策问题,依托公路路网数据库与桥梁通行标准数据库,将申请车辆的最优路径决策问题转化为轴重优化下的最优路径匹配问题。根据空间通行性、桥梁通行性、经济性和时效性指标建立了多目标优化模型,采用一种基于非线性惯性权重和动态学习因子改进的粒子群算法进行寻优求解,以某大件车辆申请数据为例验证分析。结果表明:参数优化后的大件运输车辆可以匹配原通行条件不满足要求的通行路径,新路径充分利用了公路通行能力,节约了时间成本,验证了该优化方法的可行性。

Based on the highway network database and bridge capacity database to solve the optimal traffic path decision-making problem between the origin and destination of the application for large-scale transportation vehicles.Transform the optimal path decision problem of the applied vehicle into the optimal path matching problem under the axle load optimization.Establish a multi-objective optimization model for spatial traffic,bridge traffic,economy and timeliness indicators,use an improved particle swarm algorithm based on nonlinear inertia weights and dynamic learning factors for optimization.The result shows:the new parameters can re-match paths that did not meet the original conditions.New route takes full advantage of highway capacity,saving time and cost.Verify the feasibility of this optimization method.