An Improved Real-Coded Genetic Algorithm for Synthesizing a Massive Planar Array

Abstract

The design of the Bi-State Antenna Massive Planar Array contributes to the mitigation of the energy consumption using the genetic algorithm under the constraints of minimizing the sidelobe level (SLL) and controlling the changing of the first null beamwidth (FNBW). Usually, Planar arrays are employed in communication applications based on battery usage such as portable radars. This paper optimizes a Uniform Rectangular Array (URA) with 1600 identical antenna elements using a Real-Coded Genetic Algorithm (RCGA). The optimization process is performed because the optimum set of the current excitation weight of radiating elements is found in the form of the ON-OFF state to conserve the consumed power. Hence, the highest performance of the Array Factor (AF) and the desired Beamwidth is selected. The main contribution presented in the paper is the ability to optimize a large number of array elements using the RCGA algorithm by dividing the array into a subset of arrays. The simulated results are performed to verify the effectiveness of the genetic thinned URA. The equivalent of 24.4% of the energy consumption is saved by selecting the antenna elements that could be scrambled with high effectiveness. In this paper, the results were obtained using MATLAB CAD Ver. 2018a as a platform.