Abstract
Artificial Bee Colony (ABC) is a recent meta-heuristic approach. In this paper we face the problem of clustering by ABC and we model a further bee role in the colony, performed by inspector bee. This model conforms with real honey bee colony, indeed, in nature some bees among the foraging ones are called inspectors because they preserve the colony’s history and historical information related to food sources. We experiment inspector behavior in ABC and compare the solution to traditional clustering algorithm. Finally, the effect of colony size is investigated and experimental results are discussed.
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References
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31(8), 651–666 (2010)
Hruschka, E., Campello, R.J.G.B., Freitas, A., De Carvalho, A.C.P.L.F.: A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(2), 133–155 (2009)
Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial bee colony (ABC) algorithm. Applied Soft Computing 11(1), 652–657 (2011)
Yan, X., Zhu, Y., Zou, W., Wang, L.: A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomput. 97, 241–250 (2012)
Karaboga, D.: An idea based on Honey Bee Swarm for Numerical Optimization. Technical Report TR06, Erciyes University (October 2005)
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)
Abu-Mouti, F., El-Hawary, M.: Overview of artificial bee colony (ABC) algorithm and its applications. In: 2012 IEEE International Systems Conference (SysCon), pp. 1–6 (2012)
Biesmeijer, J.C., de Vries, H.: Exploration and exploitation of food sources by social insect colonies: a revision of the scout-recruit concept. Behavioral Ecology and Sociobiology 49(2-3), 89–99 (2001)
Granovskiy, B., Latty, T., Duncan, M., Sumpter, D.J.T., Beekman, M.: How dancing honey bees keep track of changes: the role of inspector bees. Behavioral Ecology 23(3), 588–596 (2012)
Bache, K., Lichman, M.: UCI machine learning repository (2013)
Akay, B., Karaboga, D.: Parameter tuning for the artificial bee colony algorithm. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 608–619. Springer, Heidelberg (2009)
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Birtolo, C., Capasso, G., Ronca, D., Sorrentino, G. (2014). Modeling an Artificial Bee Colony with Inspector for Clustering Tasks. In: Blum, C., Ochoa, G. (eds) Evolutionary Computation in Combinatorial Optimisation. EvoCOP 2014. Lecture Notes in Computer Science, vol 8600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44320-0_16
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DOI: https://doi.org/10.1007/978-3-662-44320-0_16
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