Antenna array synthesis is one of the most popular topics in the electromagnetic field. Since achieving a desired antenna radiation pattern is a mathematical problem, in the literature, there are various optimization algorithms applied to the synthesis process of different kinds of antenna arrays. In this study, Multiverse Optimizer (MVO) and modified MVO (MMVO) are used to perform circular antenna array (CAA) synthesis. During the exploration, exploitation, and local search phases of calculation, MVO uses three concepts in cosmology; white hole, black hole, and wormhole. Convergence capability of this nature-inspired algorithm is employed for finding optimum amplitude and position values of CAA elements in order to achieve an array pattern with low maximum sidelobe level (MSL) and minimum circumference. The performance of MVO and MMVO was tested on five design examples of pattern synthesis, and the obtained results were compared with ten different algorithms. The simulation results show that MVO and MMVO provide low MSLs with small circumferences.

Antenna array synthesis is quite a popular topic in electromagnetics and finding suitable array parameter values is a challenging nonlinear engineering problem in this field. In recent years, it has been realized that optimization algorithms can be used to achieve desired solutions in this area. Besides, improvements in the optimization algorithms make it possible to perform synthesis processes on different array structures and to obtain better results. In this paper, a method based on a combination of ant lion optimizer (ALO) and sequential quadratic programming (SQP) is proposed for the concentric circular antenna array (CCAA) synthesis. Excitation amplitudes of array elements are optimized for CCAAs with low maximum sidelobe level (MSL), narrow first null beamwidth (FNBW), and low dynamic range ratio (DRR). The results of proposed ALO-SQP method are compared with those of 11 different algorithms: ALO, moth flame optimizer (MFO), SQP, symbiotic organisms search (SOS), biogeography based optimization (BBO), opposition-based gravitational search algorithm (OGSA), cat swarm optimization (CSO), firefly algorithm (FA), evolutionary programming (EP), backtracking search optimization algorithm (BSA), and seeker optimization algorithm (SOA). The comparisons indicate that the CCAAs synthesis using the proposed method presents good MSLs, FNBWs, and DRRs.