A Self–Adaptive Ant Colony System for Semantic Query Routing Problem in P2P Networks
Sistema de Colonia de Hormigas Autoadaptativo para el Problema de Direccionamiento de Consultas Semánticas en Redes P2P
Claudia Gómez Santillán1,2, Laura Cruz Reyes2, Eustorgio Meza Conde1, Elisa Schaeffer3 and Guadalupe Castilla Valdez2
1 Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada del Instituto Politécnico Nacional (CICATA– IPN). Carretera Tampico–Puerto Industrial Altamira, Km. 14.5. Altamira, Tamps., México, cggs71@hotmail.com, emezac@ipn.mx
2 Instituto Tecnológico de Ciudad Madero (ITCM). 1ro. de Mayo y Sor Juana I. de la Cruz s/n CP. 89440, Tamaulipas, México, lcruzreyes@prodigy.net.mx, gpe_cas@yahoo.com.mx
]]> 3 Facultad de Ingeniería Mecánica y Eléctrica (FIME–UANL), Avenida Universidad s/n. Cd. Universitaria, CP. 66450, San Nicolás de los Garza, N.L. México, elisa@yalma.fime.uanl.mx
Article received on July 17, 2009.
Accepted on November 05, 2009
Abstract
In this paper, we present a new algorithm to route text queries within a P2P network, called Neighboring–Ant Search (NAS) algorithm. The algorithm is based on the Ant Colony System metaheuristic and the SemAnt algorithm. More so, NAS is hybridized with local environment strategies of learning, characterization, and exploration. Two Learning Rules (LR) are used to learn from past performance, these rules are modified by three new Learning Functions (LF). A Degree–Dispersion–Coefficient (DDC) as a local topological metric is used for the structural characterization. A variant of the well–known one–step Lookahead exploration is used to search the nearby environment. These local strategies make NAS self–adaptive and improve the performance of the distributed search. Our results show the contribution of each proposed strategy to the performance of the NAS algorithm. The results reveal that NAS algorithm outperforms methods proposed in the literature, such as Random–Walk and SemAnt.
Keywords: Search Process, Internet, Complex Network, Ant Colony System, Local Environment, Neighbor.
Resumen
]]> En este documento, proponemos un nuevo algoritmo para ruteo de consultas textuales dentro de una red P2P, llamado Neighboring–Ant Search (NAS). El algoritmo está basado en la metaheurística Ant Colony System (ACS) y el algoritmo SemAnt. Además, NAS está hibridizado con estrategias del ambiente local de aprendizaje, caracterización y exploración. Dos reglas de aprendizaje (LR) son usadas para aprender del rendimiento pasado, esas reglas son modificadas por tres Funciones de Aprendizaje (LF). Un Coeficiente de Dispersión del Grado (DDC) es usado como una métrica topológica local para la caracterización estructural. Una adaptación del bien conocido método de exploración de adelanto (one–step Lookahead) es usado para explorar el ambiente cercano. Estas estrategias locales proveen a NAS una capacidad auto–adaptativa que mejora el rendimiento de la búsqueda distribuida. Los resultados experimentales muestran la contribución de cada estrategia propuesta para el rendimiento del algoritmo NAS. Estos resultados revelan que el algoritmo NAS obtiene mejores resultados que los algoritmos propuestos en la literatura existente tales como Random–Walk y SemAnt.Palabras Clave: Proceso de Búsqueda, Internet, Redes Complejas, Sistema de Colonia de Hormigas, Ambiente Local, Vecindad.
DESCARGAR ARTÍCULO EN FORMATO PDF
References
1. Amaral, L. A. N., & Ottino, J. M. (2004). Complex Systems and Networks: Challenges and Opportunities for Chemical and Biological Engineers, Chemical Engineering Scientist, 59(1), 1653–1666. [ Links ]
2. Androutsellis–Theotokis Stephanos & Diomidis Spinellis (2004). A Survey of Peer–to–Peer Content Distribution Technologies. ACM Computing Surveys, 36(4), 335–371. [ Links ]
3. Arora, S., & Barak, B. (2009). Complexity Theory: A Modern Approach. New York: Cambridge University Press. [ Links ]
4. Barabasi, A., Albert R., & Jeong, H. (1999). Mean–Field theory for Scale–free Random Networks. Physical A, 272(1), 173–189. [ Links ]
5. Cruz–Reyes, L., Gómez S. C., Aguirre L. M., Schaeffer E., Turrubiates L.T., Ortega I. R., & Fraire H. H. (2008). NAS Algorithm for Semantic Query Routing System for Complex Network. International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI2008), Advances in Soft Computing, 50, 284–292. [ Links ]
6. Di Caro, G. & Dorigo, M. (1998). AntNet: Distributed Stigmergy Control for Communications Networks. Journal of Artificial Intelligence Research, 9(1), 317–365. [ Links ]
7. Dorigo, M. & Gambardella, L. M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem, IEEE Transactions on Evolutionary Computation, 1(1), 53–66. [ Links ]
8. Erdos, P. & Rényi, A. (1960). On the Evolution of Random Graphs. Publications of the Mathematical Institute of the Hungarian Academic of Sciences. 5(1), 17–61. [ Links ]
9. Liu, J., XiaoLong, J. & Kwok, C.T. (2005). Autonomy Oriented Computing /From Problem Solving to Complex System Modeling. New York: Kluwer Academic Publisher. [ Links ]
10. Michlmayr, E. (2007). Ant Algorithms for Self–Organization in Social Networks. PhD Thesis, Vienna University of Technology. Austria, Vienna. [ Links ]
11. Mihail, M., Saberi A. & Tetali P. (2006). Random Walks with Lookahead in Power Law Random graphs. Internet Mathematics, 3(2), 147–152. [ Links ]
12. Ortega R., Meza E., Gómez C., Cruz L., & Turrubiates T. (2007). Impact of Dynamic Growing on the Internet Degree Distribution. Polish Journal of Environmental Studies, 16(1), 117–120. [ Links ]
13. Sakaryan G. (2004). A Content–Oriented Approach to Topology Evolution and Search in Peer–to–Peer Systems, PhD Thesis, University of Rostock. Rostock, Germany. [ Links ]
14. Yang, K. & Wu, C., Ho, J. (2006). AntSearch: An Ant Search Algorithm in Unstructured Peer–to–Peer Networks. IEICE Transactions on Communications, 89(9), 2300–2308. [ Links ] ]]>