Freight train operation curve optimization method based on improved multi-target grey wolf algorithm

The invention discloses a freight train operation curve optimization method based on an improved multi-target grey wolf algorithm. A homogeneous rod model and a multi-particle model based on speed self-adaption of the freight train are established according to parameter changes when the train passes through a ramp and a curve in the running process. As the coupler force of a freight train is usually large, the phenomenon of coupler breaking is likely to happen, and therefore modeling of a buffer between trains is considered. In combination with constraint conditions of a line, safety coupler force, operation time and energy consumption of a train are comprehensively considered as constraints of multi-objective optimization, driving comfort, parking accuracy and speed limiting conditions. And according to the train operation indexes and the limiting conditions of the operation road conditions, an improved multi-target grey wolf algorithm is used for researching the optimal operation working condition sequence.