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列车控制策略包括输入控制序列和每一控制序列作用距离两方面,本文建立列车运行过程多目标优化模型,以二进制和实数域的混合微粒群优化方法对该问题进行了研究,二进制微粒群算法优化列车输入控制序列,实数域微粒群算法对列车运行距离进行优化,以此得到列车最佳控制策略;针对实际的问题,提出了微粒群算法中pBest更新和gBest选择策略;并与传统的单个目标的列车运行过程优化模型进行了对比研究,仿真研究结果表明混合微粒群优化算法用于列车运行过程优化控制,可以获得满意的效果。
The train control strategy includes the input control sequence and the distance of each control sequence. In this paper, a multi-objective optimization model of train operation process is established. The hybrid particle swarm optimization method in binary and real domain is used to study the problem. The binary particle swarm optimization Train input control sequence and real number-domain particle swarm optimization algorithm to optimize the train running distance, so as to get the train optimal control strategy. In view of the actual problem, the strategy of pBest update and gBest in particle swarm optimization is proposed. Compared with the traditional single target The simulation results show that the hybrid particle swarm optimization algorithm can be used to optimize the control of train operation process, and satisfactory results can be obtained.