Application Of Harmony Search Algorithm In Automatic Operation System Of Medium-speed Maglev Train

As a kind of modern vehicle,medium-speed maglev train has become a key development direction of modern rail transit because of its small turning radius,strong climbing ability,environmental protection,low noise and relatively low energy consumption.The maglev train's automatic operation(ATO)system completes automatic train operation control according to the data set in the train schedule and train operation requirements.The timetable optimization adjustment is of great significance for the ATO system to control trains to complete automatic operation,improve train operation efficiency,ensure safe train travel and on-time arrival of trains,and improve train comfort and passenger satisfaction.However,in practical problems,the timetable optimization problem is a typical complex optimization problem,and it is difficult to find a mathematical algorithm that accurately solves the optimal solution.Harmony search algorithm is a novel intelligent optimization algorithm that simulates the musician's tuning of musical instrument tones to obtain the most beautiful harmony process.It is effective for solving complex optimization problems.The algorithm has the characteristics of low parameters,fast solution speed,strong robustness and high applicability.It is of great theoretical significance and practical value to study harmony search algorithm and apply it to the timetable optimization problem of ATO system.Firstly,this paper introduces the basic principles and research status of the harmony search algorithm.Based on this,an improved harmony search algorithm-adaptive novel global harmony search algorithm is proposed.The Benchmark function is used to test its optimization performance,and this algorithm is applied to the timetable optimization problem of the ATO system.The details are as follows:(1)An adaptive novel global harmony search algorithm is proposed to solve the HS algorithm to fall into local optimum.In order to make the algorithm achieve the best optimization effect in practical problems,seven classical test functions close to the actual problem are used to analyze the parameter settings of the algorithm for simulation experiments and determine the best setting parameters.After that,the adaptive novel global harmony search algorithm and the harmony search algorithm,improved harmony search algorithm,dynamic self-adaptive harmony search algorithm,and novel global harmony search algorithm are used to optimize the performance of the seven test functions.Comparing the optimization results of the five algorithms,it shows that the adaptive novel global harmony search algorithm has good global optimization performance and good robustness when solving the optimization problem.(2)The structural design of the operation control system and the main functions of the ATO system were studied to analyze the timetable optimization problem of the maglev train ATO system.Traditional timetables are based on operational considerations.Models are built with the objective of stopping time and interval running time.From the perspective of passengers,in this paper,the waiting time of passengers and the travel time of passengers at the station under the condition of dynamic passenger flow was considered,and finally a mathematical model based on the overall travel time of passengers as the optimization goal was established.(3)Based on the proposed mathematical model,design algorithmic solution steps.Based on planning and design of maglev test line,an adaptive novel global harmony search algorithm is applied to optimize passenger waiting time,travel time and total travel time of medium-speed maglev trains in the three operating time periods of morning and evening peaks,noon flat peaks,and morning and evening flat peaks.The validity of the ANGHS algorithm for solving the timetable optimization problem of the ATO system of the maglev train was verified.