Improving the Performance of MAX-MIN Ant System on the TSP Using Stubborn Ants
Abstract
In ant colony optimization (ACO) methods, including Ant System and MAX-MIN Ant System, each ant stochastically generates its candidate solution, in a given iteration, based on the same pheromone τ and heuristic η information as every other ant. Stubborn ants is an ACO variation in which if an ant generates a particular candidate solution in a given iteration, then the components of that solution will have a higher probability of being selected in the candidate solution generated by that ant in the next iteration. We evaluate this variation in the context of MAX-MIN Ant System using 41 instances of the Traveling Salesman Problem (TSP), and find that it improves solution quality to a statistically-significant extent.
Recommended Citation
A. M. Abdelbar and D. C. Wunsch, "Improving the Performance of MAX-MIN Ant System on the TSP Using Stubborn Ants," Proceedings of the 14th International Conference on Genetic and Evolutionary Computation (2012, Philadelphia, PA), pp. 1395 - 1396, Association for Computing Machinery (ACM), Jan 2012.
The definitive version is available at https://doi.org/10.1145/2330784.2330949
Meeting Name
14th International Conference on Genetic and Evolutionary Computation, GECCO'12 (2012: Jul. 7-11, Philadelphia, PA)
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-1450311786
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2012 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
01 Jan 2012
