Abstract
The field of optimization is abundant with algorithms, which are inspired from nature based phenomena. The increasing popularity of such algorithms stems from their applications in real life situations. Here in this article a real life problem in the form of the design of circular antenna array has been discussed. The design of the antenna array is based on the application of a novel variant of Artificial Bee Colony Algorithm using selective neighborhood called sNABC. We use a neighborhood based perturbation on the basis of Euclidean distance and fitness of individuals are used for obtaining minimum side lobe levels, maximum directivity and appropriate null control. To illustrate the effectiveness of our design procedure, the results have been compared with several existing algorithms like DE, ABC and PSO.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Godara, L.C.: Handbook of Antennas in Wireless Communications, Boca Raton, FL (2002)
Balanis, C.A.: Antenna Theory-Analysis and Design. Wiley, India (2005)
Chandran, S.: Adaptive Antenna Arrays:Trends and Applications. Springer (2004)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Panduro, M.A., Brizuela, C.A., Balderas, L.I., Acosta, D.A.: A comparison of genetic algorithms, particle swarm optimization and the differential evolution method for the design of scannable circular antenna arrays. Progress In Electromagnetics Research B 1, 171–186 (2009)
Storn, R., Price, K.: Differential Evolution- A Simple Efficient heuristic Strategy for Global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. 6th Int. Symp. Micromach. Human Sci., pp. 39–43 (1995)
Karaboga, D., Basturk, B.: A powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization 39(3) (2007)
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8, 687–697 (2008)
Mandal, A., Zafar, H., Das, S., Vasilakos, A.V.: Efficient Circular Array Synthesis with a mementic Differential Evolution Algorithm. Progress in Electromagnetics Research, 367–385 (2012)
Gurel, L., Ergul, O.: Design and simulation of circular arrays of trapezoidal-tooth log-periodic antennas via genetic optimization. Progress In Electromagnetics Research (PIER) 85, 243–260 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bose, D., Kundu, S., Biswas, S., Das, S. (2012). Circular Antenna Array Design Using Novel Perturbation Based Artificial Bee Colony Algorithm. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-35380-2_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35379-6
Online ISBN: 978-3-642-35380-2
eBook Packages: Computer ScienceComputer Science (R0)