Abstract
The analytical observation of nature induces inspiration to propose new computational paradigms to create algorithms that solve optimization and artificial intelligence problems. The artificial vision allows establishing a problem with intelligent techniques from living systems. The bioinspired systems are presented as a set of models that are based on the behavior and the way of acting of some biological systems. These models can be expressed in data mining and operations research where the clustering is a recurrent technique in the P-median problem and territorial design. On this point, we have solved clustering problems using partitioning with bioinspired aspects and variable neighborhood search to approximate optimal solutions. In this work we have improved the search strategy: we present a bioinspired partitioning algorithm with optimization by tabu search (TS). This clustering problem under a bioinspired connotation has been proposed after observing some characteristics in common between clustering and human behavior in conflict situations, where some characteristics have been modeled.
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References
Binitha, S., Sathya, S.: A survey of bio inspired optimization algorithms. Int. J. Soft Comput. Eng. (IJSCE) 2(2), 137–150 (2012) ISSN: 2231–2307
Thelma Colanzi, E., Klewerton Guez Assunção, W., Trinidad Ramirez, A.: Application of bio-inspired metaheuristics in the data clustering problem. CLEI Electron. J. 14, 1–18 (2011)
Ruiz-Vanoye, J.A., Díaz-Parra, O., Cocón, F., Soto, A., Buenabad Arias, A., Verduzco-Reyes, G., Alberto-Lira, R.: Meta-heuristics algorithms based on the grouping of animals by social behavior for the traveling salesman problem. Int. J. Comb. Optim. Probl. Inf. 3(3), 104–123 (2012). ISSN: 2007–1558
Hongbo, L., Ajith, A., Okkyungi, C., Seong, H.: Variable neighborhood particle swarm optimization for multi-objective flexible job-shop scheduling problems, SEAL 2006, LNCS, vol. 4247, pp. 197–204. Springer, Berlin, Heidelberg (2006)
Bernábe-Loranca, B., González, R., Olivares, E., Ramírez, J., Estrada, M.: A bioinspired proposal of clustering around medoids with variable neighborhood structures. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 6, 45–54 (2014). [MIR Labs, Dynamic Publishers, Inc., USA]. ISSN 2150–7988
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers (1997)
Leiva, S., Torres, F.: Una revisión de los algoritmos de partición más comunes de conglomerados: un estudio comparativo. Revista Colombiana de Estadística 33, 321–339 (2010)
Hajmohammadi, M.S.: Graph-based semi-supervised learning for cross-lingual sentiment classification. In: Intelligent Information and Database Systems, 7th Asian Conference, ACIIDS 2015, pp. 91–110. Bali, Indonesia (2015)
Jain, S., Swamy, C., Balaji, K.: Greedy algorithms for k-way graph partitioning
Resende, M., Werneck, R.: On the implementation of a swap-based local search procedure for the p-median problem. In: Proceedings of the 5th Workshop on Algorithm Engineering and Experiments (ALENEX), pp. 119–127 (2003)
Project Management Committee. GeoTools The Open Source Java GIS Toolkit. http://geotools.org/. Accessed 14 Aug 2015
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Bernábe-Loranca, M.B., Velazquez, R.G., Analco, M.E., Ruíz-Vanoye, J., Penna, A.F., Sánchez, A. (2016). Bioinspired Tabu Search for Geographic Partitioning. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_17
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DOI: https://doi.org/10.1007/978-3-319-27400-3_17
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