Research On Train Speed Curve Optimization Based On Heuristic Genetic Algorithms

With the development of the railway industry,rail transit equipment represented by highspeed rail has become a shining national business card in China.Therefore,it is urgent to relieve the energy consumption of trains.How to effectively reduce the energy consumption of trains has always been a key scientific issue for researchers.In order to effectively reduce the energy consumption of trains and improve the intelligence level of trains,the researchers have carried out in-depth research on control strategies and operational simulations by establishing various mathematical models and adding various optimization algorithms.The research of the thesis mainly includes the following parts:(1)According to the force situation of the train under various conditions and the heuristic rules obtained by summarizing the driving experience and prior knowledge of many excellent train drivers,the corresponding train operation model is established.In addition,energy sensitivity analysis is performed on some of the operational resistances during running process;(2)Briefly introduce the relevant information and operation process of classical genetic algorithm,and analyze its advantages and disadvantages in the optimization of train running curve.Improve the genetic algorithm according to its inadequacies,and introduce the design ideas and improvement points of the improved genetic algorithm in detail;(3)Through the simulation example,the situation of the various lines is analyzed,and the improvement and advantages of the algorithm and other algorithms are analyzed and compared,and the optimization details are described in detail.For many optimization algorithms,the robustness is poor and the optimization effect is not ideal under the limit line conditions.However,the conventional evolutionary algorithms are inefficient and easy to fall into local optimum.A train energy-saving operation based on improved genetic algorithm is proposed Optimization Strategy.In the Matlab R2016 b simulation environment,the improved genetic algorithm is used to optimize the simulation of the train operation;the simulation experiment results show the algorithm has the advantages of fast convergence speed and high robustness,and can achieve the purpose of effectively reducing the energy consumption of train operation.In particular,it effectively overcomes the shortcomings of the evolutionary algorithm's search result uncertainty and speed volatility,and has good reference and practical value for energy-saving operation and automatic driving in this field and other vehicles.