At present, China’s urban railway is faced with a large number of insufficient maintenance of failed components and surplus maintenance of normal components. It is urgent to carry out research on the generation of maintenance strategy of urban railway. As to satisfy the demand of transportation capacity in different periods, improve the safety of urban railway operation, and reduce the cost of urban railway maintenance. Particle swarm optimization is a stochastic global optimization method. It can be applied to find the optimal region in the complex search space by the interaction between particles. Particle swarm optimization algorithm is improved through position and particle variation similarity, which is presented in this paper. Based on this improved algorithm, a maintenance strategy generation for urban railway is proposed. The results from a case study show that the operation cost can be reduced by using the improved particle swarm optimization algorithm.


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    Title :

    Maintenance Strategy Generation for Urban Railway Based on Improved Particle Swarm Optimization Algorithm


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qin, Yong (editor) / Jia, Limin (editor) / Liu, Baoming (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / He, Zhichao (author) / Xia, Zhicheng (author) / Wang, Yanhui (author) / Li, Lijie (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2019 ; Qingdao, China October 25, 2019 - October 27, 2019



    Publication date :

    2020-04-04


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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