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A Modified Genetic Algorithm for Multi-Objective Optimization on Running Curve of Automatic Train Operation System Using Penalty Function Method

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Abstract

The running curve optimization of Automatic Train Operation system usually takes into account running time, energy consumption and passenger comfort. In this paper, in order to provide more comprehensive optimization and accurate reference of running curve for Automatic Train Operation system, we adopted the multi-objective optimization strategy of genetic algorithm to optimize from five aspects: speeding (safety), parking accuracy, punctuality, energy consumption and comfort. In order to increase the convergence speed of genetic algorithm to the optimal solutions, we propose a modified genetic algorithm, which the penalty function method is added into the fitness objective function. The modified genetic algorithm optimization program is written by M language in MATLAB, and combined with a graphical user interface tool to design the optimization system. Its validity is verified by comparison between the tests based on three different interstation of Shanghai Metro Line 11. The results show that it is effective and practicability to use the designed system to optimize the running curve of Automatic Train Operation system.

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Acknowledgements

This work is supported by the National “Twelfth Five-Year” Pillar program for Science & Technology – the Interoperability Comprehensive Evaluation Integrative Platform and Demonstration for Urban Rail Transit (No.2015BAG19B02).

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Authors and Affiliations

  1. Department of Electrical Traction & Control, Institute of Rail Transit, Tongji University, Building H112, No.4800 Cao’an Road, Jiading District, Shanghai, China

    Yanchu Liang, Hao Liu, Cunyuan Qian & Guanlei Wang

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  1. Yanchu Liang
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  2. Hao Liu
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  3. Cunyuan Qian
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  4. Guanlei Wang
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Correspondence to Cunyuan Qian.

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Liang, Y., Liu, H., Qian, C. et al. A Modified Genetic Algorithm for Multi-Objective Optimization on Running Curve of Automatic Train Operation System Using Penalty Function Method. Int. J. ITS Res. 17, 74–87 (2019). https://doi.org/10.1007/s13177-018-0158-6

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  • DOI: https://doi.org/10.1007/s13177-018-0158-6

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