Manipulation Strategy and Operation Optimization Algorithm for Medium/Low Speed Maglev Trains
In order to improve the operation optimization of medium/low speed maglev train line with large gradients and complex operating environments,a method was proposed based on the improved dung beetle optimizer.Firstly,the"Multiple Coasting"manipulation strategy was proposed in response to the line characteristics.Secondly,an optimization model for medium/low speed maglev train operation that takes into account punctuality,energy efficiency and comfort was established.Then,the Cubic chaotic mapping and opposition-based learning strategies were used to initialize the population of the dung beetle optimizer,while a linear adaptive population allocation ratio was designed to improve the convergence speed of the algorithm.Finally,a benchmark test function was used to test the algorithm against the four population intelligence optimization algorithms,with an example simulation conducted based on a maglev line.The ex-perimental results show that the improved algorithm,with good optimization performance in solving complex functions and effective improvement of the performance indicators in the example simulation,has good reference and practical value.
medium/low speed magnetic levitationmanipulation strategyoperational optimizationdung beetle optimizer
万方数据
维普
