Abstract
Traveling Salesman Problem (TSP) is a typical combinatorial optimization problem, and it is a NP-hard problem. The total number of routes increases exponentially with the number of cities, so it is great significance to design an effective algorithm to find the optimal solution accurately. Chicken Swarm Optimization (CSO) is a new intelligent optimization algorithm, which is mainly proposed for continuous problems. It has the advantages of fast convergence speed and high convergence accuracy. This paper proposed a Discrete Chicken Swarm Optimization (DCSO) for TSP. The CSO is discretized by introducing the methods of swap, order crossover and reverse order mutation, where the search space of the solution is enlarged, and the diversity of the solution is increased. The typical TSP models are simulated and compared with the Basic Ant Colony Optimization and Genetic Algorithm to verify the feasibility of the presented method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
