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.
关键词
中低速磁浮/操纵策略/运行优化/蜣螂优化算法
Key words
medium/low speed magnetic levitation/manipulation strategy/operational optimization/dung beetle optimizer
维普
万方数据