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

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...

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Bibliographic Details
Published in:Sustainability Vol. 13; no. 3; p. 1408
Main Authors: Liu, Fuying, Liu, Chen, Zhao, Qi, He, Chenhao
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.02.2021
Subjects:
Algorithms
Cities
Decision making
Light rail transit
Mathematical models
Mathematical optimization
Population
Route optimization
Sales promotions
Teaching
Tourist attractions
Transportation terminals
Travel
Travel industry
ISSN:2071-1050, 2071-1050
Online Access:Get full text
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Summary: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.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su13031408