Abstract: Meta-heuristic evolutionary algorithms have become widely used for solving complex optimization problems. However, their effectiveness in real-world applications is often limited by the need for many evaluations, which can be both costlyand time-consuming. This is especially true for large-scale transportation networks, where the size of the problem and the high computational cost can hinder the algorithm's performance. To address these challenges, recent research has focused on using surrogate-assisted models. These models  aim to reduce the number of expensive evaluations and improve the efficiency of solving time-consuming optimization problems. This paper presents a new two-layer Surrogate-Assisted Fish Migration Optimization (SA-FMO) algorithm designed to tackle high-dimensional and computationally heavy problems. The global surrogate model offers a good approximation of the entire problem space, while the local surrogate model focuses on refining the solution near the current best option, improving local optimization. To test the effectiveness of the SA-FMO algorithm, we first conduct experiments using six benchmark functions in a 50-dimensional space. We then apply the algorithm to optimize urban rail transit routes, focusing on the Train Routing Optimization problem. This aims to improve operational efficiency and vehicle turnover in situations with uneven passenger flow during transit disruptions. The results show that SA-FMO can effectively improve optimization outcomes in complex transportation scenarios.

Key words: Train routing optimization')">Train routing optimization, Surrogate-assisted, Fish migration optimization, Meta-heuristic evolutionary algorithm