Research on Multi-objective Optimization of Freight Train Operation Process Based on Improved Bald Eagle Search Algorithm

Research on Multi-objective Optimization of Freight Train Operation Process Based on Improved Bald Eagle Search Algorithm

To study the optimization problem of freight train operation process under complex line conditions, this paper adopts the freight train multi-particle model to establish the multi-objective optimization model of the freight train. Aiming at the problem of difficult optimization caused by complex line conditions, a two-step method is used to find the optimal operation strategy of the freight train. Firstly, the greedy algorithm is used to select the optimal working condition sequence. Secondly, the multi-objective optimization algorithm is used to obtain the position of the ideal working condition transition point. By introducing the idea of non-dominated sorting, an improved multi-objective bald eagle search algorithm is proposed to optimize the operation process of freight trains. This algorithm adopts the evolution method of combining bald eagle population renewal with adaptive Gaussian mutation, and introduces preference information to increase the rationality of population evolution. The simulation results show that the optimal operation strategy of freight train selected by two-step method is in line with the actual operation situation, and the proposed multi-objective bald eagle search algorithm considers both convergence and distribution. The results can satisfy the preference of decision-makers, which can provide a reference target speed curve for railway workers.