A hybrid teaching-learning-based optimization algorithm for the travel route optimization problem alongside the urban railway line

Abstract

Accurate travel route optimization is essential to promote and grow tourism in modern society. This paper investigates a travel route optimization problem alongside the urban railway line and proposes a hybrid teaching–learning-based optimization (HTLBO) algorithm. First, a mathematical programming model is established to minimize the total traveling time, in which the routes between and in different cities have to be appropriately determined. Then, a hybrid metaheuristic named HTLBO is proposed for solution generation. In HTLBO, depth first search (DFS) is utilized to obtain the optimal routes of any two stations in railway network, and a three-level coding method is designed to accommodate the problem characteristic. Besides, opposition-based learning (OBL) is embedded into teaching-learning-based optimization (TLBO) for enhancing HTLBO’s exploration ability, while variable neighborhood descent (VND) is used to enhance the algorithm’s exploitation ability. Finally, a case study is presented and simulation results verify HTLBO’s feasibility and effectiveness.