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列车优化调度是一个大规模、复杂的、具有非线性离散变量和多约束的多目标数学优化问题.在优化过程中,考虑了特快旅客列车中途离开时间和整个运行时间等因素.首次将粒子群优化(particle swarmoptimization,PSO)技术引入列车优化调度,克服了传统优化方法易陷入局部最优和维数灾难等弊端.通过一个工程实例验证了该算法的可行性和有效性.同时,与现存的列车优化调度方法相比,粒子群优化方法的搜索时间短而且优化结果更接近最优解.
The optimization of train scheduling is a large-scale, complex, multi-objective mathematical optimization problem with nonlinear discrete variables and multiple constraints.In the optimization process, the factors such as the departure time of the express passenger train and the entire running time are taken into account.The particle swarm optimization The algorithm of particle swarm optimization (PSO) is introduced into train optimization scheduling to overcome the drawbacks of the traditional optimization methods, such as easy fall into local optimum and dimensionality disaster, etc. The feasibility and effectiveness of this algorithm are verified by an engineering example.At the same time, Compared with the train scheduling method, the particle swarm optimization method has shorter searching time and the optimization result is closer to the optimal solution.