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Application of Artificial Bee Colony Algorithms to Antenna Design Problems for RFID Applications

Application of Artificial Bee Colony Algorithms to Antenna Design Problems for RFID Applications

Sotirios K. Goudos, Katherine Siakavara, John N. Sahalos
Copyright: © 2016 |Pages: 30
ISBN13: 9781466696440|ISBN10: 1466696443|EISBN13: 9781466696457
DOI: 10.4018/978-1-4666-9644-0.ch009
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MLA

Goudos, Sotirios K., et al. "Application of Artificial Bee Colony Algorithms to Antenna Design Problems for RFID Applications." Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, edited by Pandian Vasant, et al., IGI Global, 2016, pp. 236-265. https://doi.org/10.4018/978-1-4666-9644-0.ch009

APA

Goudos, S. K., Siakavara, K., & Sahalos, J. N. (2016). Application of Artificial Bee Colony Algorithms to Antenna Design Problems for RFID Applications. In P. Vasant, G. Weber, & V. Dieu (Eds.), Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics (pp. 236-265). IGI Global. https://doi.org/10.4018/978-1-4666-9644-0.ch009

Chicago

Goudos, Sotirios K., Katherine Siakavara, and John N. Sahalos. "Application of Artificial Bee Colony Algorithms to Antenna Design Problems for RFID Applications." In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, edited by Pandian Vasant, Gerhard-Wilhelm Weber, and Vo Ngoc Dieu, 236-265. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9644-0.ch009

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Abstract

In this chapter, the Artificial Bee Colony (ABC) algorithm and its variants are presented and applied to spiral antennas design for RFID tag application at the UHF band. The ABC variants include the Improved ABC (I-ABC), which is an improved version of the original ABC algorithm. The I-ABC introduces the best-so-far solution, inertia weight and acceleration coefficients to modify the search process. Furthermore, another ABC variant is the Gbest ABC (ABC), which includes global best (gbest) solution information into the search equation to improve the exploitation. These algorithms are applied to antenna design where the optimization goals are antenna size minimization, gain maximization, and conjugate matching. The algorithms performance is compared with other popular evolutionary algorithms. The optimization results produced show that the ABC family of algorithms is a powerful tool that can be efficiently applied to antenna design problems.

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