QPSO versus BSA for Failure Correction of Linear Array of Mutually Coupled Parallel Dipole Antennas with Fixed Side Lobe Level and VSWR

Abstract

Evolutionary algorithms play an important role in synthesizing linear array antenna. In this paper, the authors have compared quantum particle swarm optimization (QPSO) and backtracking search algorithms (BSA) in failure correction of linear antenna arrays constructed using half wavelength uniformly spaced dipoles. The QPSO algorithm is a combination of classical PSO and quantum mechanics principles to enhance the performance of PSO and BSA is considered as a modernized PSO using historical populations. These two algorithms are applied to obtain the voltage excitations of the nondefective elements in the failed antenna array such that the necessary objectives, namely, the minimization of parameters like side lobe level (SLL) and voltage standing wave ratio (VSWR), are achieved leading to their values matching closely the desired parameter values. The results of both algorithms are compared in terms of parameters used in the objective function along their statistical parameters. Moreover, in order to reduce the processing time, inverse fast Fourier transform (IFFT) is used to obtain the array factor. In this paper, an example is presented for the application of the above two algorithms for a linear array of 30 parallel half wavelength dipole antennas failed with 4 elements and they clearly show the effectiveness of both the QPSO and BSA algorithms in terms of optimized parameters, statistical values, and processing time.