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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Fuying Liu & Chen Liu & Qi Zhao & Chenhao He, 2021. "A Hybrid Teaching-Learning-Based Optimization Algorithm for the Travel Route Optimization Problem alongside the Urban Railway Line," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1408-:d:489516
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    References listed on IDEAS

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