Abstract
In many science fields such as physics, chemistry and engineering, the theory and experimentation complement and challenge each other. Algorithms are the most common form of problem solving in many science fields. All algorithms include parameters that need to be tuned with the objective of optimizing its processes. The NAS (Neighboring-Ant Search) algorithm was developed to route queries through the Internet. NAS is based on the ACS (Ant Colony System) metaheuristic and SemAnt algorithm, hybridized with local strategies such as: learning, characterization, and exploration. This work applies techniques of Design of Experiments for the analysis of NAS algorithm. The objective is to find out significant parameters for the algorithm performance and relations among them. Our results show that the probability distribution of the network topology has a huge significance in the performance of the NAS algorithm. Besides, the probability distributions of queries invocation and repositories localization have a combined influence in the performance.
This research was supported in part by CONACYT, DGEST and IPN.
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Santillán, C.G., Reyes, L.C., Conde, E.M., Martinez, C.A., Lam, M.A.A., Zezzatti, C.A.O.O. (2009). Performance Analysis of the Neighboring-Ant Search Algorithm through Design of Experiment. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_80
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DOI: https://doi.org/10.1007/978-3-642-02319-4_80
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