A 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.

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This article presents optimization of circular arrays with mutual coupling effect (MCE) using newly developed algorithm, Improved Salp swarm optimization (ISSO). ISSO is employed to achieve optimum radiation patterns of circular array (CA), and concentric circular array (CCA). The radiation patterns illustrate directive main beams with various outcomes, such as suppressed maximum side lobe level, deep nulls placed in specific directions, and maximum possible directivity. The main beams of the patterns are made narrow and directive by maintaining the first null beam widths equal or less than that of uniform CA and CCA throughout the optimization process. The above performance parameters are obtained by optimizing the parameters like excitation currents, positions, and phases of the array elements, and number of array elements using a multi-objective optimization function (OF). The OF is formulated by considering the performance parameters and MCE. The outcomes are analyzed for eight different optimization case studies of CA and CCA. The results of the CA and CAA using ISSO are compared with that of other optimization methods found in the literature. The performance parameters of the case studies considered indicate that CA configurations using ISSO offer improved performance relative to uniform arrays and the configurations achieved using other methods of optimization.