Energy Consumption Optimization for High-Speed Railway Based on Particle Swarm Algorithm

From the point of the perspective of train control strategies, energy saving for high-speed railway will be explored in this paper. The energy consumption of high-speed railway is mainly used for train operation, accounting for about 87%. This paper definitely presents a particle swarm algorithm to compute the energy consumption, which aims to reduce the railway energy by obtaining optimal train control strategies. The algorithm establishes a fresh mathematical model, setting energy consumption, running time and stop accuracy as objects, setting limited velocity and motion as constraint condition, and develops an improved adaptive novel multi-population particle swarm with novel crossover and mutation strategies, in order to reduce the computational complexity and ensure the accuracy of the energy consumption results. Over all, a simulation system has been built to resolute problems of high-speed railway. According to the simulation results, the algorithm is proved to be efficient and helpful on energy saving.

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