A clonal selection based differential evolution algorithm for double-track railway train schedule optimization

The train scheduling is a crucial part of modern railway operation system. The train schedule optimization problem is exceptionally large-scale and hardly solved via the conventional mathematical method. In this paper, the double-track railway train schedule optimization is concerned. Based on the characteristics of the double-track railway operation system, a novel train schedule optimization model based on clonal selection differential evolution (CSDE) algorithm is proposed. In comparison to the traditional differential evolution method, the modifications include: (1) clonal selection mechanism with an auxiliary pool is introduced to provide the best individuals with more evolution opportunities; (2) an apoptosis process is employed in which worst individuals will be either extinct or regenerated in probability; (3) the population is in a dynamic balance. The novel method contains more control variables and can be converted into ordinary differential evolution problem with specific parameters setting. In a computational instance, the implementing result indicates that the proposed model and algorithm are practical and feasible for double-track train schedule optimization problem.