改进粒子群算法的轨道列车节能控制优化
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华东交通大学

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U292.4+3

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江西省教育厅科技项目(GJJ160490)


Study on Energy-saving Control of Railway Train Based on Improved PSO
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    摘要:

    当前列车节能控制研究的优化算法,存在优化效果不明显,收敛速度慢等一系列问题,提出了一种,引入自适应惯性权重,同时加入具有调整能力的动态学习因子与改进速度更新公式的粒子群优化算法。一是通过惯性权重,来平衡不同阶段的搜索能力,加入拥有调整能力的动态学习因子,着重加强算法后期的运算效率和收敛能力,同时引入惩罚函数,将列车运行过程中的拘束条件转化为惩罚因子,提升搜索速率;二是对传统的速度更新公式进行改进,用来降低选取到不理想的粒子影响寻优结果的概率。经过Matlab仿真分析后,与传统的列车运行优化算法相比,改进后的优化算法收敛速度更快,列车节能效果更好。

    Abstract:

    At present, there are a series of problems in the optimization algorithm of train energy-saving control, such as the effect of optimization is not obvious and the convergence speed is slow. A PSO algorithm with adaptive inertia weight, learning factor with adjustable ability and improved speed update formula is proposed. Firstly, inertia weight is used to balance the search ability in different stages, and dynamic learning factor with adjustable ability is added to enhance the operation efficiency and convergence ability of the algorithm in the later stage, at the same time, penalty function is introduced to transform the constraints in train operation into penalty factor, which can improve the search rate. The second is to improve the traditional velocity update formula to reduce the probability of selecting unsatisfactory particles affecting the optimization results. After simulation and analysis by Matlab, compared with the traditional train operation optimization algorithm, the improved optimization algorithm converges faster and has better energy-saving effect.

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历史
  • 收稿日期:2019-09-16
  • 最后修改日期:2019-10-21
  • 录用日期:2019-11-04
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