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Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine 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)

  • Zhao Sheng

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

  • Senkui Lu

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

Abstract

In this paper, an improved multi-objective shark smell optimization algorithm using composite angle cosine is proposed for automatic train operation (ATO). Specifically, when solving the problem that the automatic train operation velocity trajectory optimization easily falls into local optimum, the shark smell optimization algorithm with strong searching ability is adopted, and composite angle cosine is incorporated. In addition, the dual-population evolution mechanism is adopted to restrain the aggregation phenomenon in shark population at the end of the iteration to suppress the local convergence. Correspondingly, the composite angle cosine, considering the numerical difference and preference difference, is used as the evaluation index, which ameliorates the shortcoming that the traditional evaluation index is not objective and reasonable. Finally, the Matlab/simulation and hardware-in-the-loop simulation (HILS) results for automatic train operation show that the improved optimization algorithm proposed in this paper has better optimization performance.

Suggested Citation

  • Longda Wang & Xingcheng Wang & Zhao Sheng & Senkui Lu, 2020. "Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation," Energies, MDPI, vol. 13(3), pages 1-25, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:714-:d:317537
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    References listed on IDEAS

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    1. Kaddani, Sami & Vanderpooten, Daniel & Vanpeperstraete, Jean-Michel & Aissi, Hassene, 2017. "Weighted sum model with partial preference information: Application to multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 260(2), pages 665-679.
    2. Zhaoxiang Tan & Shaofeng Lu & Kai Bao & Shaoning Zhang & Chaoxian Wu & Jie Yang & Fei Xue, 2018. "Adaptive Partial Train Speed Trajectory Optimization," Energies, MDPI, vol. 11(12), pages 1-33, November.
    3. Ahmadigorji, Masoud & Amjady, Nima, 2016. "A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorithm," Energy, Elsevier, vol. 102(C), pages 199-215.
    4. Podinovskii, Vladislav V., 1994. "Criteria importance theory," Mathematical Social Sciences, Elsevier, vol. 27(3), pages 237-252, June.
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    Cited by:

    1. Ehab S. Ali & Sahar. M. Abd Elazim & Sultan H. Hakmi & Mohamed I. Mosaad, 2023. "Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems," Energies, MDPI, vol. 16(10), pages 1-27, May.
    2. Luis B. Elvas & Joao C Ferreira, 2021. "Intelligent Transportation Systems for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-9, September.

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