Chebyshev-based array for beam steering and null positioning using modified ant lion optimization

Abstract

AbstractA modified ant lion optimization (MALO) algorithm is proposed in this article, for the synthesis of Chebyshev-based arrays by optimizing amplitudes and phases of excitations, and element spacings. Modification in ant lion optimization is achieved by hybridizing it with chaotic particle swarm optimization. The optimization process is employed to obtain an array pattern with the least possible sidelobe level. Close-in sidelobe level minimization for optimum pattern synthesis is suggested. Instead of only steering the main beam towards the desired direction presented by some popular optimization methods, the beam steering along with null positioning in other specified direction is also achieved employing MALO. Considering the arrays with the same design parameters and the results of other optimization algorithms, the performance of MALO is evaluated. The results show that MALO provides considerable improvements in an array pattern compared to the arrays optimized using other optimization algorithms and the uniform array.