A Hybrid Teaching-Learning-Based Optimization Algorithm for the Travel Route Optimization Problem alongside the Urban Railway Line

• Main Authors: Fuying Liu, Chen Liu, Qi Zhao, Chenhao He
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Sustainability
• Subjects: optimization; metaheuristic; teaching-learning-based optimization; depth first search; variable neighborhood descent
• Online Access: https://www.mdpi.com/2071-1050/13/3/1408

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.