Abstract
In this paper, we present our version of the MultiObjective Artificial Bee Colony algorithm (a metaheuristic based on the foraging behavior of honey bees) to optimize the Location Areas Planning Problem. This bi-objective problem models one of the most important tasks in any Public Land Mobile Network: the mobile location management. In previous works of other authors, this management problem was simplified by using the linear aggregation of the objective functions. However, this technique has several drawbacks. That is the reason why we propose the use of multiobjective optimization. Furthermore, with the aim of studying a realistic mobile environment, we apply our algorithm to the mobile network developed by the Stanford University (a mobile network located in the San Francisco Bay, USA). Experimental results show that our proposal outperforms other algorithms published in the literature.
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Berrocal-Plaza, V., Vega-Rodríguez, M.A., Sánchez-Pérez, J.M. (2013). A New Version of the Multiobjective Artificial Bee Colony Algorithm for Optimizing the Location Areas Planning in a Realistic Network. In: Dediu, AH., Martín-Vide, C., Truthe, B., Vega-Rodríguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2013. Lecture Notes in Computer Science, vol 8273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45008-2_2
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DOI: https://doi.org/10.1007/978-3-642-45008-2_2
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