EPE  Vol.13 No.4 B , April 2021
Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization
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.
Cite this paper: Yi, L. , Duan, R. , Li, W. , Wang, Y. , Zhang, D. and Liu, B. (2021) Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization. Energy and Power Engineering, 13, 41-51. doi: 10.4236/epe.2021.134B005.

[1]   Khmelnitsky, E. (2000) On an Optimal Control Problem of Train Operation. IEEE Transactions on Automatic Control, 45, 1257-1266.

[2]   Liang, Y., Liu, H., Qian, C. and Wang, G. (2018) A Modified Genetic Algorithm for Multi-Objective Optimization on Running Curve of Automatic Train Operation System Using Penalty Function Method. International Journal of Intelligent Transportation Systems Research, 17, 74-87.

[3]   Wang, L. Wang, X. and Sun, D. (2017) Multi-Objective Optimization Improved GA Algorithm and Fuzzy PID Control of ATO System for Train Operation. International Conference on Intelligent Computing for Sustainable Energy and Environment, Singapore, 23 August 2017, 13-22.

[4]   Dullinger, C., Struckl, W. and Kozek, M. (2017) Simulation-Based Multi-Objective System Optimization of Train Traction Systems. Simulation Modelling Practice and Theory, 72, 104-117.

[5]   Wang, L., Wang, X., Liu, K. and Sheng, Z. (2019) Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation. Energies, 12, 1882.

[6]   Coello, C.A.C. and Lechuga, M.S. (2002) MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization. Proceedings of the 2002 Congress on Evolutionary Computation, 1051-1056.

[7]   Dominguez, M., Fernandez-Cardador, A. and Cuclal, A.P. (2014) Multi Objective Particle Swarm Optimization Algorithm for the Design of Efficient ATO Speed Profiles in Metro Lines. Engineering Applications of Artificial, 29, 43-53.

[8]   Hu, W. and Yen, G.G. (2015) Adaptive Multiobjective Particles Swarm Optimization Based on Parallel Cell Coordinate System. IEEE Transactions on Evolutionary Computation, 19, 1-18.

[9]   Peng, W. and Zhang, Q.A. (2008) A Decomposition-Based Multi-Objective Particle Swarm Optimization Algorithm for Continuous Optimization Problems. IEEE International Conference on Granular Computing, 534-537.

[10]   Cheng, R. and Jin, Y. (2015) A Social Learning Particle Swarm Optimization Algorithm for Scalable Optimization. Information Sciences, 291, 43-60.