Synthesis and Design of Thinned Planar Concentric Circular Antenna Array - A Multi-objective Approach

  • Sk. Minhazul Islam
  • Saurav Ghosh
  • Subhrajit Roy
  • Shizheng Zhao
  • Ponnuthurai Nagaratnam Suganthan
  • Swagamtam Das
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7077)


Thinned concentric antenna array design is one of the most important electromagnetic optimization problems of current interest. This antenna must generate a pencil beam pattern in the vertical plane along with minimized side lobe level (SLL) and desired HPBW, FNBW and number of switched off elements. In this article, for the first time to the best of our knowledge, a multi-objective optimization framework for this design is presented. Four objectives described above we are treated as four distinct objectives that are to be optimized simultaneously. The multi-objective approach provides greater flexibility by yielding a set of equivalent final solutions from which the user can choose one that attains a suitable trade-off margin as per requirements. In this article, we have used a multi-objective algorithm of current interest namely the NSGA-II algorithm. There are two types of design, one with uniform inter-element spacing fixed at 0.5λ and the other with optimum uniform inter-element spacing. Extensive simulation and results are given with respect to the obtained HPBW, SLL, FNBW and number of switched off elements and compared with two state-of-the-art single objective optimization methods namely DE and PSO.


Particle Swarm Optimization Differential Evolution Antenna Array Side Lobe Level Multiobjective Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Elliott, R.S.: Antenna Theory and Design, 2nd edn. John Wiley, New Jersey (2003)CrossRefGoogle Scholar
  2. 2.
    Dessouky, M.I., Sharshar, H.A., Albagory, Y.A.: Efficient sidelobe reduction technique for small-sized concentric circular arrays. Progress in Electromagnetics Research 65, 187–200 (2006)CrossRefGoogle Scholar
  3. 3.
    Dessouky, M.I., Sharshar, H.A., Albagory, Y.A.: Optimum normalized-Gaussian tapering window for side lobe reduction in uniform concentric circular arrays. Progress in Electromagnetics Research 69, 35–46 (2007)CrossRefGoogle Scholar
  4. 4.
    Zhao, S.Z., Suganthan, P.N.: Two-lbests Based Multi-objective Particle Swarm Optimizer. Engineering Optimization 43(1), 1–17 (2011), doi:10.1080/03052151003686716MathSciNetCrossRefGoogle Scholar
  5. 5.
    Qu, B.Y., Suganthan, P.N.: Multi-Objective Evolutionary Algorithms based on the Summation of Normalized Objectives and Diversified Selection. Information Sciences 180(17), 3170–3181 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning, vol. 41. Addison-Wesley (1989)Google Scholar
  7. 7.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Conf. Neural Networks IV, Piscataway, NJ (1995)Google Scholar
  8. 8.
    Zhang, Q., Li, H.: MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Trans. Evolutionary Computation, 712–731 (2007)Google Scholar
  9. 9.
    Das, S., Suganthan, P.N.: Differential Evolution: A Survey of the State-of-the-Art. IEEE Trans. Evolutionary Computation 15(1), 4–31 (2011)CrossRefGoogle Scholar
  10. 10.
    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),
  11. 11.
    Deb, K., Pratap, A., Garwal, S.A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  12. 12.
    Pal, S., Das, S., Basak, A., Suganthan, P.N.: Synthesis of difference patterns for monopulse antennas with optimal combination of array-size and number of subarrays - A multi-objective optimization approach. Progress in Electromagnetics Research, PIER B 21, 257–280 (2010)Google Scholar
  13. 13.
    Pal, S., Qu, B.Y., Das, S., Suganthan, P.N.: Optimal Synthesis of Linear Antenna Arrays with Multi-objective Differential Evolution. Progress in Electromagnetics Research, PIER B 21, 87–111 (2010)Google Scholar
  14. 14.
    Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P.N., Zhang, Q.: Multi-objective Evolutionary Algorithms: A Survey of the State-of-the-art. Swarm and Evolutionary Computation 1(1), 32–49 (2011)CrossRefGoogle Scholar
  15. 15.
    Abido, M.A.: A novel multi-objective evolutionary algorithm for environmental/economic power dispatch. Electric Power Systems Research 65, 71–81 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sk. Minhazul Islam
    • 1
  • Saurav Ghosh
    • 1
  • Subhrajit Roy
    • 1
  • Shizheng Zhao
    • 2
  • Ponnuthurai Nagaratnam Suganthan
    • 2
  • Swagamtam Das
    • 1
  1. 1.Dept. of Electronics and Telecommunication Engg.Jadavpur UniversityKolkataIndia
  2. 2.Dept. of Electronics and Electrical Engg.Nanyang Technological UnivrsitySingapore

Personalised recommendations