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Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation

Author

Listed:
  • Longda Wang

    (School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China)

  • Xingcheng Wang

    (School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China)

  • Kaiwei Liu

    (School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China)

  • Zhao Sheng

    (School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Aiming at the problem of easy-to-fall-into local convergence for automatic train operation (ATO) velocity ideal trajectory profile optimization algorithms, an improved multi-objective hybrid optimization algorithm using a comprehensive learning strategy (ICLHOA) is proposed. Firstly, an improved particle swarm optimization algorithm which adopts multiple particle optimization models is proposed, to avoid the destruction of population diversity caused by single optimization model. Secondly, to avoid the problem of random and blind searching in iterative computation process, the chaotic mapping and the reverse learning mechanism are introduced into the improved whale optimization algorithm. Thirdly, the improved archive mechanism is used to store the non-dominated solutions in the optimization process, and fusion distance is used to maintain the diversity of elite set. Fourthly, a dual-population evolutionary mechanism using archive as an information communication medium is designed to enhance the global convergence improvement of hybrid optimization algorithms. Finally, the optimization results on the benchmark functions show that the ICLHOA can significantly outperform other algorithms for contrast. Furthermore, the ATO Matlab/simulation and hardware-in-the-loop simulation (HILS) results show that the ICLHOA has a better optimization effect than that of the traditional optimization algorithms and improved algorithms.

Suggested Citation

  • Longda Wang & Xingcheng Wang & Kaiwei Liu & Zhao Sheng, 2019. "Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation," Energies, MDPI, vol. 12(10), pages 1-33, May.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1882-:d:232028
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    Citations

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    Cited by:

    1. Artur Kierzkowski & Szymon Haładyn, 2022. "Method for Reconfiguring Train Schedules Taking into Account the Global Reduction of Railway Energy Consumption," Energies, MDPI, vol. 15(5), pages 1-18, March.
    2. Franciszek Restel & Szymon Mateusz Haładyn, 2022. "The Railway Timetable Evaluation Method in Terms of Operational Robustness against Overloads of the Power Supply System," Energies, MDPI, vol. 15(17), pages 1-17, September.

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