基于微粒群算法的多目标列车运行过程优化

为客观地描述列车的运行过程,建立了列车运行过程的多目标优化模型,并用微粒群算法求解该模型.针对多目标微粒群优化(MOPSO)算法的不足,提出了相应的改进措施和解的多样性保持策略.仿真结果表明,提出的优化列车运行过程的改进MOPSO算法可以在一次运行过程中获得多组列车操纵控制策略,清晰地显示出各性能指标随控制策略变化的趋势,控制序列转换次数大大降低,每组控制策略都可以在能耗、运行时间和停靠准确性之间获得很好的折衷效果,可以根据列车运行状况选择恰当的策略控制列车,以获得预期的结果.

Multi-objective Train Operation Optimization Based on Particle Swarm Algorithm

To reveal the essence of multiple objectives of train operation, a multi-objective model for train operation was established and solved by using the multi-objective optimization method. Improvement and keeping diversity strategies were introduced to overcome the deficiencies of the existing MOPSO (multi-objective particle swarm optimization) algorithms. Simulation results show that the improved MOPSO algorithm can generate more than one train control strategy during a time running simultaneously, display changes in performance indices with the control strategies and decrease the shifting number of control serials sharply. Furthermore, fine tradeoff among energy cost, running time and stopping at adequate point can be obtained, as a result, the strategy suited to the train running can be selected to get an anticipated result.