Multi-objective Optimization Strategy of Train ATO Based on Improved Grey Wolf Algorithm

In order to meet the requirements of safety, energy conservation, punctuality, comfort and other indicators in the operation process of train ATO, a multi-objective optimization strategy based on improved Grey Wolf Optimizer (GWO) algorithm was proposed. Firstly, based on the train operation control...

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Vydané v:2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) s. 565 - 570
Hlavný autor: Jiang, Minjian
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 12.04.2024
Predmet:
Automatic Train Operation
Computers
Energy conservation
Energy consumption
Heuristic algorithms
Image processing
improved grey wolf algorithm
Mathematical models
multi-objective optimization
Optimization
Safety
Testing
train operation control
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Shrnutí:In order to meet the requirements of safety, energy conservation, punctuality, comfort and other indicators in the operation process of train ATO, a multi-objective optimization strategy based on improved Grey Wolf Optimizer (GWO) algorithm was proposed. Firstly, based on the train operation control strategy and dynamic equations, an evaluation function for performance indicators such as energy consumption, punctuality, comfort, and parking error of train ATO was constructed, and a multi-objective optimization mathematical model for the train was established; Then, based on the principles and drawbacks of the GWO algorithm, the initialization and position update methods were improved, enhancing the optimization ability of the GWO algorithm. The improved GWO algorithm was validated through testing functions to effectively compensate for the shortcomings of the GWO algorithm; Finally, the improved GWO algorithm was applied to multi-objective optimization of train ATO, and simulation experiments showed that the energy consumption, actual running time, comfort value, and parking error of train operation were significantly improved, verifying the effectiveness of this strategy.
DOI:10.1109/IPEC61310.2024.00102