Abstract
Ant Colony Optimization is a metaheuristic which has been successfully applied to solve several NP-hard problems. It includes several algorithms which imitate the behavior of natural ants. The algorithm called Ant Colony System is one of the best-performing ant-based algorithms. In this paper we present an enhanced algorithm, which applies dynamic programming to improve the solution generated by the ants. The method is applied to the well-known Traveling Salesman Problem. We present computational results that show the improvement obtained with the modified algorithm. © 2009 Springer Berlin Heidelberg.
Author supplied keywords
Cite
CITATION STYLE
Pérez-Delgado, M. L., & Burrieza, J. E. (2009). A post-optimization method to improve the ant colony system algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5518 LNCS, pp. 424–431). https://doi.org/10.1007/978-3-642-02481-8_60
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
