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