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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
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.
References
Bagajewicz, M., Sánchez, M.: Cost optimal design and upgrade of non-redundant and redundant linear sensor networks. AIChE J. 45(9), 1927–1938 (1999)
Baluja, S.: Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical report-CMU-CS-94163, Carnegie Mellon University, Pittsburgh, PA (1994)
Bello-Orgaz, G., Salcedo-Sanz, S., Camacho, D.: A multi-objective genetic algorithm for overlapping community detection based on edge encoding. Inf. Sci. 462, 290–314 (2018)
Carnero, M., Hernández, J., Sánchez, M.: A new metaheuristic based approach for the design of sensor networks. Comput. Chem. Eng. 55, 83–96 (2013)
Carnero, M., Hernández, J., Sánchez, M., Bandoni, A.: An evolutionary approach for the design of nonredundant sensor networks. Indus. Eng. Chem. Res. 40(23), 5578–5584 (2001)
Carnero, M., Hernández, J.L., Sánchez, M.: Optimal sensor location in chemical plants using the estimation of distribution algorithms. Indus. Eng. Chem. Res. (2018). https://doi.org/10.1021/acs.iecr.8b01680
Gonzalez-Pardo, A., Ser, J.D., Camacho, D.: Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems. Appl. Soft Comput. 60, 241–255 (2017)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Engineering Faculty, Computer Engineering Department, Erciyes University (2005)
Karaboga, D., Akay, B.: A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl. Soft Comput. 11(3), 3021–3031 (2011)
Kashan, M.H., Nahavandi, N., Kashan, A.H.: DisABC: a new artificial bee colony algorithm for binary optimization. Appl. Soft Comput. 12(1), 342–352 (2012)
Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M.D., Camacho, D.: Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms. Soft Comput. 21(17), 4883–4900 (2017)
Romagnoli, J., Sánchez, M.: Data Processing and Reconciliation for Chemical Process Operations. Academic Press, Cambridge (2000)
Sen, S., Narasimhan, S., Deb, K.: Sensor network design of linear processes using genetic algorithms. Comput. Chem. Eng. 22(3), 385–390 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-03496-2_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03495-5
Online ISBN: 978-3-030-03496-2
eBook Packages: Computer ScienceComputer Science (R0)