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

Fuying Liu, Chen Liu, Qi Zhao and Chenhao He
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Fuying Liu: JangHo Architecture College, Northeastern University, Shenyang 110819, China
Chen Liu: School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
Qi Zhao: JangHo Architecture College, Northeastern University, Shenyang 110819, China
Chenhao He: JangHo Architecture College, Northeastern University, Shenyang 110819, China

Sustainability, 2021, vol. 13, issue 3, 1-17

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.

Keywords: optimization; metaheuristic; teaching-learning-based optimization; depth first search; variable neighborhood descent (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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