Non-uniform circular-shaped antenna array design and synthesis - a multi-objective approach

  • Authors:

  • Saurav Ghosh;Subhrajit Roy;Sk. Minhazul Islam;Shizheng Zhao;Ponnuthurai Nagaratnam Suganthan;Swagatam Das

  • Affiliations:

  • Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Dept. of Electronics and Electrical Engg., Nanyang Technological Univrsity, Singapore;Dept. of Electronics and Electrical Engg., Nanyang Technological Univrsity, Singapore;Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India

  • Venue:
  • SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
  • Year:
  • 2011

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Abstract

Design of non-uniform circular antenna arrays is one of the important optimization problems in electromagnetic domain. While designing a non-uniform circular array the goal of the designer is to achieve minimum side lobe levels with maximum directivity. In contrast to the single-objective methods that attempt to minimize a weighted sum of the four objectives considered here, in this article we consider these as four distinct objectives that are to be optimized simultaneously in a multi-objective (MO) framework using one of the best known Multi-Objective Evolutionary Algorithms (MOEAs) called NSGA-II. This MO approach provides greater flexibility in design by producing a set of final solutions with different trade-offs among the four objective from which the designer can choose one as per requirements. To the best of our knowledge, other than the single objective approaches, no MOEA has been applied to design a non-uniform circular array before. Simulations have been conducted to show that the best compromise solution obtained by NSGA-II is far better than the best results achieved by the single objective approaches by using the differential evolution (DE) algorithm and the Particle Swarm Optimization (PSO) algorithm.