Abstract
For the sequence of five working conditions in urban rail transit lines, generating the train recommend speed curve is established into a multi-objective optimization problem including safety, punctuality, parking accuracy, comfort, and energy consumption. The working condition conversion point data is used as the target to be optimized. An adaptive global particle swarm optimization (AGPSO) algorithm is proposed. Furthermore, AGPSO and two variants of PSO are used to solve the multi-objective optimization problem. The results show that only the optimization results of AGPSO meet the requirements of various indicators of automatic train driving system (ATO) control strategy.
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Hu, Z., Xiao, X., Bao, F. (2020). Adaptive Global Particle Swarm Algorithm-Based Train Recommend Speed Curve Optimization Study in Urban Rail Transit. In: Liu, B., Jia, L., Qin, Y., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering, vol 640. Springer, Singapore. https://doi.org/10.1007/978-981-15-2914-6_10
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DOI: https://doi.org/10.1007/978-981-15-2914-6_10
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