Reduce side lobes using linear Antenna Arrays by comparing PSO, GA, and FPA algorithms

Abstract

Linear Antenna Arrays (LAAs) are widely used electromagnetic systems in modern wireless communication, and Metaheuristics algorithms have been utilized to reduce side lobe level SLL and reach the optimal solution. This paper employs three algorithms: the first, Particle Swarm Optimization PSO, the second, Genetic Algorithm GA, and the third, Flower Pollination Algorithm FPA. Each test consists of N = 8, 16, 32, 64, 128, and 256 antenna array elements. To reduce SLL and the concentration of radioactive energy in the main lobe, each algorithm compares the beam pattern to the theoretical beam pattern. In addition, the algorithms were compared with the existence of the theoretical beam pattern, and it was discovered that there is a superior algorithm for each number of antenna elements; in N = 8, when comparing FPA to other algorithms, it was discovered that FPA reduced SLL by a value of -20.8492dB, which was superior to the other algorithms. SLL decreased by -27.2992dB when comparing PSO with other algorithms at N = 16. When N = 32,64 represents FPA more accurately than other algorithms where the SLL plummeted to -28.3071dB and -28.0148dB, respectively. GA is superior to other algorithms when N = 128,256, reducing SLL by -28.5568 dB and -28.6204 dB, respectively.