Improving the Performance of MAX-MIN Ant System on the TSP Using Stubborn Ants

Ashraf M. Abdelbar; Donald C. Wunsch

doi:10.1145/2330784.2330949

Improving the Performance of MAX-MIN Ant System on the TSP Using Stubborn Ants

Ashraf M. Abdelbar
, Donald C. Wunsch

Research output: Contribution to journal › Article › peer-review

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.

Original language	American English
Journal	Proceedings of the 14th International Conference on Genetic and Evolutionary Computation (2012, Philadelphia, PA)
DOIs	https://doi.org/10.1145/2330784.2330949
State	Published - Jan 1 2012

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Electrical and Computer Engineering

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https://doi.org/10.1145/2330784.2330949

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title = "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.",

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N2 - 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.

AB - 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.

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/459

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JF - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation (2012, Philadelphia, PA)

ER -

Improving the Performance of MAX-MIN Ant System on the TSP Using Stubborn Ants

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