Abstract
In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.
Highlights
Railway transport is an indispensable mode of transportation
In view of the complex problems that freight train ATO needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation
Reference [7] based on the subway train, the multi-objective particle swarm optimization algorithm is used to obtain the non-inferior solution of energy consumption and time in the ATO system, taking into account the comfort of passengers
Summary
Railway transport is an indispensable mode of transportation. With the continuous increase of the scale of the railway transport network in our country, the freight operation is becoming more and more onerous, and the requirements for the comprehensive performance of train operation are getting higher and higher. According to the characteristics of train operation, the dynamic model of train operation process is established in [3], and the linear weight method and multi-objective genetic algorithm are used to solve it. In view of the problem that the train speed track is easy to fall into local convergence in [5], an improved multi-objective hybrid optimization method based on comprehensive learning strategy is proposed, which has good advantages. In [6], a multi-objective particle swarm optimization algorithm is proposed to solve the multi-objective optimization problem. Reference [7] based on the subway train, the multi-objective particle swarm optimization algorithm is used to obtain the non-inferior solution of energy consumption and time in the ATO system, taking into account the comfort of passengers. The effectiveness of the algorithm is verified by experimental simulation results
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