基于集成粒子群优化的复线旅客列车优化调度

列车优化调度是一个大规模、复杂的数学优化问题。在优化过程中，考虑了特快旅客列车中途离开时间、普快列车中途离开时间和特快、普快和货车等三种列车的整个运行时间等因素。提出将模拟退火优化方法嵌入粒子群优化算法中，以此构建集成粒子群优化算法．在搜索过程中还加入变异探作来增加种群多样性，以避免早熟收敛．通过对青岛至广东高速轨道线738公里段的研究表明，集成粒子群优化算法局部搜索能力有显著提高，且搜索到全局最优解的概率更大。

Bi-criteria Passenger Trains Scheduling Optimal Planning Based on Integrated Particle Swarm Optimization

The train scheduling for high-speed passenger railroad planning is formulated as a mathematical optimization problem. Three objectives: the variation of inter-departure times for high-speed and medium-speed trains and the total travel time of high-speed and medium-speed trains and freight trains were considered in the optimization. An integrated particle swarm optimization (IPSO) was proposed, which the simulated annealing optimization algorithm is combined with PSO to speed up the local search, also mutation operation is embedded to avoid the common defect of premature convergence. The performance of new algorithm was demonstrated through a numerical example of the 738 km portion of Qingdao-Guangzhou high-speed rail line and the obtained results show that the local search ability is improved, and the probability of finding the global optimal value by IPSO is larger than that by using other algorithms.