基于协同粒子群优化算法的列车动态编组研究

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中图分类号：U283;U279;TP391.9 文献标志码：B doi：10.20213/j.cnki.tdcl.2025.01.016

Abstract：In the field of train operation control，dynamic marshaling technology is receiving increasing atention.With the goal of reducing the energy consumption of train traction，this paper proposes a multipopulation cooperative particle swarm optimization control method based on nonlinear weights，which focuses on the acuracy of marshalling as wellas the anti-interference ability. The dynamic marshalling model is first established，folowed by the dynamic marshaling optimization，aiming for low-energy consumption and accurate marshaling. The train operation is controlled using the MCPSO-NW algorithm and the particle swarm algorithm，respectively. The simulation results show that the MCPSO-NW algorithm exhibits greater stability，enhances the marshaling precision，and consumes less energy.It is capable of reasonably adjusting the train speed under external interference，achieving the goal of low-energy consumption，so as to meet the needs of energy saving and emission reduction.

Key words：train control; dynamic marshalling；nonlinear weight；multi-population collaboration；particle swarm

铁路作为一种重要的运输形式，在我国交通运输 体系中处于核心地位。（剩余8805字）