This paper deals with both the leading train and the following train in a train tracking under a four-aspect fixed autoblock system in order to study the optimum operating strategy for energy saving. After analyzing t...This paper deals with both the leading train and the following train in a train tracking under a four-aspect fixed autoblock system in order to study the optimum operating strategy for energy saving. After analyzing the working principle of the four-aspect fixed autoblock system, an energy-saving control model is created based on the dynamics equation of the Wains. In addition to safety, energy consumption and time error are the main concerns of the model. Based on this model, dynamic speed constraints of the following train are proposed, defined by the leading gain dynamically. At the same time, the static speed constraints defined by the line conditions are also taken into account. The parallel genetic algorithm is used to search the optimum operating strategy. In order to simplify the solving process, the external punishment function is adopted to transform this problem with constraints to the one without constraints. By using the real number coding and the strategy of dividing ramps into three parts, the convergence of GA is accelerated and the length of chromosomes is shortened. The simulation result from a four-aspect fixed autoblock system simulation platform shows that the method can reduce the energy consumption effectively in the premise of ensuring safety and punctuality.展开更多
针对调度指挥控制培训系统(Dispatch Command and Control Training System,DCCS)存在的响应延迟高、资源利用率低等问题,提出一种融合人工蜂群算法(Artificial Bee Colony,ABC)与粒子群优化算法(Particle Swarm Optimization,PSO)的混...针对调度指挥控制培训系统(Dispatch Command and Control Training System,DCCS)存在的响应延迟高、资源利用率低等问题,提出一种融合人工蜂群算法(Artificial Bee Colony,ABC)与粒子群优化算法(Particle Swarm Optimization,PSO)的混合优化方法。该方法结合PSO的快速局部收敛能力与ABC的强全局探索特性,旨在提升系统调度效率与资源分配精度。实验结果表明,所提算法在典型调度任务下的平均收敛时间为2.3 s,显著优于单一ABC与PSO算法。基于该算法优化后的DCCS系统响应时间缩短至1.3 s,调度准确率由68.5%提升至91.7%(绝对提升23.2%)。结果表明,该优化方法能有效提升DCCS的响应速度与决策准确性,为高保真、高效率的调度指挥培训提供技术支撑。展开更多
基金supported by the National Science & Technology Pillar Program during the Eleventh Five-Year Plan Period of China (No.2009BAG12A05)
文摘This paper deals with both the leading train and the following train in a train tracking under a four-aspect fixed autoblock system in order to study the optimum operating strategy for energy saving. After analyzing the working principle of the four-aspect fixed autoblock system, an energy-saving control model is created based on the dynamics equation of the Wains. In addition to safety, energy consumption and time error are the main concerns of the model. Based on this model, dynamic speed constraints of the following train are proposed, defined by the leading gain dynamically. At the same time, the static speed constraints defined by the line conditions are also taken into account. The parallel genetic algorithm is used to search the optimum operating strategy. In order to simplify the solving process, the external punishment function is adopted to transform this problem with constraints to the one without constraints. By using the real number coding and the strategy of dividing ramps into three parts, the convergence of GA is accelerated and the length of chromosomes is shortened. The simulation result from a four-aspect fixed autoblock system simulation platform shows that the method can reduce the energy consumption effectively in the premise of ensuring safety and punctuality.
文摘针对调度指挥控制培训系统(Dispatch Command and Control Training System,DCCS)存在的响应延迟高、资源利用率低等问题,提出一种融合人工蜂群算法(Artificial Bee Colony,ABC)与粒子群优化算法(Particle Swarm Optimization,PSO)的混合优化方法。该方法结合PSO的快速局部收敛能力与ABC的强全局探索特性,旨在提升系统调度效率与资源分配精度。实验结果表明,所提算法在典型调度任务下的平均收敛时间为2.3 s,显著优于单一ABC与PSO算法。基于该算法优化后的DCCS系统响应时间缩短至1.3 s,调度准确率由68.5%提升至91.7%(绝对提升23.2%)。结果表明,该优化方法能有效提升DCCS的响应速度与决策准确性,为高保真、高效率的调度指挥培训提供技术支撑。
