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Electromagnetic Antenna Configuration Optimization Using Fitness Adaptive Differential Evolution

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6466))

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Abstract

In this article a novel numerical technique, called Fitness Adaptive Differential Evolution (FiADE) for optimizing certain pre-defined antenna configuration is represented. Differential Evolution (DE), inspired by the natural phenomenon of theory of evolution of life on earth, employs the similar computational steps as by any other Evolutionary Algorithm (EA). Scale Factor and Crossover Probability are two very important control parameter of DE since the former regulates the step size taken while mutating a population member in DE. This article describes a very competitive yet very simple form of adaptation technique for tuning the scale factor, on the run, without any user intervention. The adaptation strategy is based on the fitness function value of individuals in DE population. The feasibility, efficiency and effectiveness of the proposed algorithm for optimization of antenna problems are examined by a set of well-known antenna configurations.

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References

  1. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  2. Kirkpatrick, S., Gellat Jr., C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–679 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  3. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kauffman, San Francisco (2001)

    Google Scholar 

  4. Rahmat-Samii, Y., Michielssen, E. (eds.): Electromagnetic Optimization by Genetic Algorithms. Wiley, New York (1999)

    MATH  Google Scholar 

  5. Coleman, C., Rothwell, E., Ross, J.: Investigation of simulated annealing, ant-colony optimization, and genetic algorithms for self-structuring antennas. IEEE Trans. Antennas Propag. 52, 1007–1014 (2004)

    Article  Google Scholar 

  6. Storn, R., Price, K.: Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  7. Storn, R., Price, K.V.: Differential Evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012,ICSI (1995), http://http.icsi.berkeley.edu/~storn/litera.html

  8. Liu, J., Lampinen, J.: On setting the control parameters of the differential evolution method. In: Matoušek, R., Ošmera, P. (eds.) Proc. of Mendel 2002, 8th International Conference on Soft Computing, pp. 11–18 (2002)

    Google Scholar 

  9. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization”. IEEE Transactions on Evolutionary Computation 13(2), 398–417 (2009)

    Article  Google Scholar 

  10. Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self-adapting Control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)

    Article  Google Scholar 

  11. Pantoja, M.F., Bretones, A.R., Martin, R.G.: Benchmark Antenna Problems for Evolutionary Optimization Algorithms. IEEE Transaction on Antennas and Propagation 55(4), 1111–1121 (2007)

    Article  Google Scholar 

  12. Balanis, C.A.: Antenna Theory. Analysis and Design, 2nd edn. Wiley, New York (1997)

    Google Scholar 

  13. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)

    Google Scholar 

  14. Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics 1, 355–366 (2006)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Electronics and Telecommunication Engg, Jadavpur University, Kolkata, 700 032, India

    Aritra Chowdhury, Arnob Ghosh, Ritwik Giri & Swagatam Das

  2. Machine Intelligence Resarch Labs (MIR Labs) Global, USA

    Aritra Chowdhury, Arnob Ghosh, Ritwik Giri & Swagatam Das

Authors
  1. Aritra Chowdhury
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  2. Arnob Ghosh
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  3. Ritwik Giri
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  4. Swagatam Das
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical and Electronics Engineering, SRM University, Chennai, Tamil Nadu, India

    Subhransu Sekhar Dash

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© 2010 Springer-Verlag Berlin Heidelberg

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Chowdhury, A., Ghosh, A., Giri, R., Das, S. (2010). Electromagnetic Antenna Configuration Optimization Using Fitness Adaptive Differential Evolution. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-17563-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17562-6

  • Online ISBN: 978-3-642-17563-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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