Abstract
We propose a new model of ant colony optimization (ACO) to solve the traveling salesman problem (TSP) by introducing ants with memory into the ant colony system (ACS). In the new ant system, the ants can remember and make use of the best-so-far solution, so that the algorithm is able to converge into at least a near-optimum solution quickly. We have tested the algorithm in 3 representational TSP instances and compared the results with the original ACS algorithm. According to the result we make amelioration to the new ant model and test it again. The simulations show that the amended ants with memory improve the converge speed and can find better solutions compared to the original ants.
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.
