Abstract
The sensor network design problem (SNDP) consists of the selection of the type, number and location of the sensors to measure a set of variables, optimizing a specified criteria, and simultaneously satisfying the information requirements. This problem is multimodal and involves several binary variables, therefore it is a complex combinatorial optimization problem. This paper presents a new Artificial Bee Colony (ABC) algorithm designed to solve high scale designs of sensor networks. For this purpose, the proposed ABC algorithm has been designed to optimize binary structured problems and also to handle constraints to fulfil information requirements. The classical version of the ABC algorithm was proposed for solving unconstrained and continuous optimization problems. Several extensions have been proposed that allow the classical ABC algorithm to work on constrained or on binary optimization problems. Therefore the proposed approach is a new version of the ABC algorithm that combines the binary and constrained optimization extensions to solve the SNDP. Finally the new algorithm is tested using different systems of incremental size to evaluate its quality, robustness, and scalability.
This work has been co-funded by the following research projects: EphemeCH (TIN2014-56494-C4-4-P), DeepBio (TIN2017-85727-C4-3-P) projects (Spanish Ministry of Economy and Competitivity, under the European Regional Development Fund FEDER) and in part by the Justice Programme of the European Union (2014-2020) 723180, RiskTrack, under Grant JUST-2015-JCOO-AG and Grant JUST-2015-JCOO-AG-1.
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Notes
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The interested reader can get access to the files containing information about the case studies from https://drive.google.com/file/d/1FvPwDxW06xhcrEcX7RgUhMV0Eh4lrY1p/view?usp=sharing.
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Panizo, Á., Bello-Orgaz, G., Carnero, M., Hernández, J., Sánchez, M., Camacho, D. (2018). An Artificial Bee Colony Algorithm for Optimizing the Design of Sensor Networks. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_35
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