Date of Award
Spring 2023
Rights
Access is available to all users
Document Type
Thesis
Degree Name
Master of Science (MS) in Computer Science
Department
Computer Science
Abstract
The ant colony optimization algorithm (ACO) is a fast heuristic-based method for finding favorable solutions to the traveling salesman problem (TSP). When the data set reaches larger values however, the ACO runtime increases dramatically. As a result, clustering nodes into groups is an effective way to reduce the size of the problem while leveraging the advantages of the ACO algorithm. The method for recombining groups of nodes is explored by treating the graph as a hierarchy of clusters, and modifying the original ACO heuristic to operate on a hypergraph. This method of using hierarchical clustering is significantly faster than the original ACO algorithm, even when normal clustering techniques are applied, while producing improved tour lengths.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Fischer, Bryan J., "A hierarchical approach to improve the ant colony optimization algorithm" (2023). EWU Masters Thesis Collection. 845.
https://dc.ewu.edu/theses/845
