Optimization of high-speed train control strategy for traction energy saving using an improved genetic algorithm. (English) Zbl 1442.49049

Summary: A parallel multipopulation genetic algorithm (PMPGA) is proposed to optimize the train control strategy, which reduces the energy consumption at a specified running time. The paper considered not only energy consumption, but also running time, security, and riding comfort. Also an actual railway line (Beijing-Shanghai High-Speed Railway) parameter including the slop, tunnel, and curve was applied for simulation. Train traction property and braking property was explored detailed to ensure the accuracy of running. The PMPGA was also compared with the standard genetic algorithm (SGA); the influence of the fitness function representation on the search results was also explored. By running a series of simulations, energy savings were found, both qualitatively and quantitatively, which were affected by applying cursing and coasting running status. The paper compared the PMPGA with the multiobjective fuzzy optimization algorithm and differential evolution based algorithm and showed that PMPGA has achieved better result. The method can be widely applied to related high-speed train.


49N90 Applications of optimal control and differential games
49M05 Numerical methods based on necessary conditions
93C95 Application models in control theory
Full Text: DOI


[1] Prakash Bhardwaj, V.; Nitin, On the minimization of crosstalk conflicts in a destination based modified omega network, Journal of Information Processing Systems, 9, 2, 301-314 (2013)
[2] Hui Chong, J.; Kyun Ng, C.; Kamariah Noordin, N.; Mohd Ali, B., Dynamic transmit antenna shuffling scheme for MIMO wireless communication systems, Journal of Convergence, 4, 1 (2013)
[3] Masoumi, S.; Tabatabaei, R.; Feizi-Derakhshi, M.-R.; Tabatabaei, K., A new parallel algorithm for frequent pattern mining, Journal of Computational Intelligence and Electronic Systems, 2, 1, 55-59 (2013)
[4] Kumar Gupta, H.; Singhal, P. K.; Sharma, G.; Patidar, D., Rectenna system design in L-band (1-2 GHz) 1. 3 GHz for wireless power transmission, Journal of Computational Intelligence and Electronic Systems, 1, 2, 149-153 (2012)
[5] Yang, L.; Hu, Y.; Sun, L., Energy-saving track profile of urban mass transit, Journal of Tongji University, 40, 2, 235-240 (2012)
[6] Bocharnikov, Y. V.; Tobias, A. M.; Roberts, C.; Hillmansen, S.; Goodman, C. J., Optimal driving strategy for traction energy saving on DC suburban railways, IET Electric Power Applications, 1, 5, 675-682 (2007)
[7] Chen, J.-F.; Lin, R.-L.; Liu, Y.-C., Optimization of an MRT train schedule: reducing maximum traction power by using genetic algorithms, IEEE Transactions on Power Systems, 20, 3, 1366-1372 (2005)
[8] Milroy, I. P., Aspects of automatic train control [Ph.D. thesis] (1980), Loughborough University
[9] Howlett, P. G., Existence of an optimal strategy for the control of a train, School of Mathematics Report, #3 (1988), University of South Australida
[10] Kawakami, T., Integration of heterogeneous systems, Proceedings of the Fourth International Symposium on Autonomous Decentralized Systems
[11] Cheng, J.-X., Modeling the energy-saving train control problems with a long-haul train, Journal of System Simulation, 11, 4 (1999)
[12] Hwang, H. S., Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway, IEEE Transactions on Systems, Man, and Cybernetics A: Systems and Humans, 28, 6, 791-802 (1998)
[13] Songbai, T., Study on the running resistance of Quasi-high speed passenger trains, Science of China Railways, 18, 1 (1997)
[14] Zhongyang, Z.; Zhongyang, S., Analysis of additional resistance calculation considering the length of the train and discuss of the curve additional resistance clause in the Traction Regulations, Railway Locomotive & Car, 2 (2000)
[15] Golovitcher, I., An analytical method for optimum train control computation, Izvestiya Vuzov Seriya Electrome Chanica, 3, 59-66 (1986)
[16] Khmelnitsky, E., On an optimal control problem of train operation, Institute of Electrical and Electronics Engineers. Transactions on Automatic Control, 45, 7, 1257-1266 (2000) · Zbl 0972.49026
[17] Singh, B.; Krishan Lobiyal, D., A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks, Human-Centric Computing and Information Sciences, 2 (2012)
[18] Yan, X. H.; Cai, B. G.; Ning, B., Research on multi-objective high-speed train operation optimization based on differential evolution, Journal of the China Railway Society, 35, 9 (2013)
[19] Wang, D. C.; Li, K. P.; Li, X., Multi-objective energy-saving train scheduling model based on fuzzy optimization algorithm, Science Technology and Engineering, 12, 12 (2012)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.