A Post-optimization Method to Improve the Ant Colony System Algorithm

Authors:
M. L. Pérez-Delgado;J. Escuadra Burrieza
Affiliations:
Universidad de Salamanca. Escuela Politécnica Superior de Zamora, Zamora, Spain C.P. 49022;Universidad de Salamanca. Escuela Politécnica Superior de Zamora, Zamora, Spain C.P. 49022
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Year:
2009

Citing 3
Cited 0

Ant Colony Optimization

Ant Colony Optimization
The traveling salesman: computational solutions for TSP applications

The traveling salesman: computational solutions for TSP applications
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation

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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.