# Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
## 1 Introduction
### Related Work  
In [26] [PERSON] and [PERSON], apply an AI-based solution approach for transit route system planning and design, consisting of three major components: a route generation design algorithm, an analysis procedure, and a route improvement algorithm. In [27] [PERSON] and [PERSON] try to solve the urban transit routing problem using a differential evolution approach. [PERSON] and [PERSON] [28] apply a complete hierarchical multi-objective genetic algorithm for the transit network design problem, while [PERSON] and [PERSON] [29] propose a hybrid differential evolution-particle swarm optimization approach to tackle the multiobjective urban transit network design problem with homogeneous buses. In [30] [PERSON] et al. try to cope with the transport network design problem using a many-objective evolutionary approach, while in [31], [PERSON] and [PERSON] use genetic algorithms in order to construct an \"optimal\" set of routes. The information used is link travel times and transit demands. The proposed procedure performs better than existing techniques. Moreover, [PERSON] et al. propose in [32] a multi-objective meta-heuristic approach for transit network design and the frequency setting problem in a bus transit system, while [PERSON] et al. present in [33] a complete review an comparative analysis of modern metaheuristic approaches for the transit route network design problem. In [34], [PERSON] et al. present and try to solve a transit route network design problem considering equity, while in [35], [PERSON] et al. present a model and an effective solution approach to the electric transit route network design problem. [PERSON] et al. present a solution to the UTRP based on an evolutionary multi-objective optimization technique [15]. They try to minimize both costs (passenger and operator). As benchmark instances, they use the [PERSON]'s network as well as a larger transport network. [PERSON] tried also to solve [PERSON]'s UTRP instance using a heuristic algorithm [36]. Moreover, a framework to solve UTRP has been introduced by [PERSON] and [PERSON] [37]. They evaluate candidate route sets by devising a simple model of the UTRP and validate their approach using hill-climbing and simulated annealing algorithms. Their method was applied to [PERSON]'s network and achieved significant improvement. [PERSON] et al. presented a multi-objective optimization model consisting of both passengers' and operators' perspectives [15]. They also apply their algorithm to obtain optimal results on [PERSON]'s network. In [17], a genetic algorithm is applied to cope with the UTRP. The authors use a group of parameters to evaluate the route sets, while the main goal is to minimize passenger costs. [PERSON] et al. [38], developed a population-based model applying a genetic algorithm optimization strategy. [PERSON] and [PERSON] in [39] presented a demand-based public transit route generation algorithm. [PERSON] and [PERSON] ([40; 41]) simultaneously determine the links to be included in the transit network, assemble chosen links into bus routes, and determine bus frequency on each of the designed routes, applying a bee colony optimization algorithm. [PERSON] et al. introduced in [42] a memetic algorithm approach, imbedded with a local search operator based on the classical genetic algorithm approach, to improve the results as well as the computational performance. [PERSON] [13] presented a multi-objective evolutionary framework and introduced some new genetic operators which are problem specific. She also presented a new heuristic method to seed the genetic population with feasible route sets. Finally, she made some new datasets available for experimentation and performance comparison. [PERSON] and [PERSON] applied a hybrid particle swarm algorithm which produced, up to that point, the best route sets for [PERSON]'s network [43].