An Improved Particle Swarm Optimization Algorithm for the Urban Transit Routing Problem

The Urban Transit Routing Problem (UTRP) is a challenging discrete problem that revolves around designing efficient routes for public transport systems. It falls under the category of NP-hard problems, characterized by its complexity and numerous constraints. Evaluating potential route sets feasibility demanding time-consuming task, often resulting in rejection many solutions. Given difficulty, metaheuristic methods, such as swarm intelligence algorithms, are considered highly suitable addressing UTRP. However, effectiveness these methods depends heavily on appropriately adapting them to well employing initialization procedures solution-evaluation methods. In this study, new variant particle optimization algorithm proposed an solution approach We present improved function modification operators, along with post-optimization routine further improve algorithm’s performance then compared state art using Mandl’s widely recognized benchmark, standard evaluating UTRP By comparing generated solutions published results from 10 studies benchmark network, we demonstrate developed outperforms existing techniques, providing superior outcomes.