th International Conference on Intelligent Systems Application of Firefly Algorithm to Train Operation
th International Conference on Intelligent Systems Application of Firefly Algorithm to Train Operation
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第六届萤火虫算法在列车运行智能系统应用国际会议
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
A. Karamancioglu
中科院分区：
文献类型：
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作者：
Kemal Keskin;A. Karamancioglu
对能源的需求不断增加以及对环境的担忧，使得节能运输的重要性日益凸显。在本文中，研究了一种基于寻找最优速度曲线的节能列车运行方式。作为一种受自然启发的元启发式方法，萤火虫算法被用于寻找列车控制信号的最优切换点。在问题的构建中，能耗被作为目标函数的主要部分，而运行时间作为惩罚因子被包含在内。为了验证所得到的结果，进行了一次模拟。除了萤火虫算法，还使用了遗传算法来比较结果。这两种算法在具有不同坡度曲线的测试轨道上进行了多次模拟。它们都考虑了列车运动的四个阶段（最大加速、巡航、惰行和制动）。此外，通过放宽边界条件，算法能够组织不包括巡航阶段的运动阶段。模拟结果表明，与遗传算法相比，萤火虫算法能提供更准确和稳定的解决方案。此外，它能够在较少的迭代次数内收敛到解决方案，因此适用于实时问题的解决。
关键词—萤火虫算法；列车运行；能源效率
Increasing demand for energy and environmental concerns have increased importance of energy-efficient transportation. In this manuscript, an energy-efficient train operation based on finding optimal speed profile is studied. As a natureinspired metaheuristic approach, Firefly algorithm is employed to find optimal switching points of the train control signal. In problem formulation, energy consumption is taken as major part of objective function and travel time is being included as penalty factor. In order to verify the obtained results, a simulation is performed. Besides firefly algorithm, genetic algorithm is also used to compare results. Two algorithms are simulated on test track with various grade profiles for several times. Both of them considered four phases (maximum acceleration, cruising, coasting and braking) of train motion. Furthermore with the help of relaxing boundary conditions, algorithms are able to organize motion phases excluding cruising phase. Simulation results demonstrated that, compared to the GA, FA provides more accurate and persistent solutions. In addition, it can be converged to solution in small iterations, so it is compatible for using in real time problem solving. Keywords—Firefly algorithm, train operation, energy efficiency