An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.

A backtracking search optimization algorithm (BSA) is proposed for the synthesis of concentric circular antenna arrays (CCAAs) with the low sidelobe levels at a fixed beamwidth. Several numerical examples of CCAA patterns with the single, multiple, and broad nulls imposed at the directions of interference are also given to illustrate the performance and flexibility of the proposed algorithm. BSA is a relatively new population based evolutionary optimization algorithm. The numerical results show that the design of CCAA using BSA provides good sidelobe levels with a fixed beamwidth. The nulling patterns obtained by BSA are also very good. The quality of results obtained by the BSA is checked by comparing with that of several metaheuristic algorithms in the literature. BSA is also compared with differential search (DS) and bacterial foraging algorithms (BFA) in terms of iterative performances.

In this study, political optimizer (PO) which has entered the literature as a novel socio-inspired meta-heuristic method is used to improve radiation properties of concentric circular antenna arrays (CCAAs) in the far field such as wireless communication of smart grids and Internet of things. The radiation pattern of far field is improved by reducing the sidelobe level (SLL), which is the most important parameter of the antenna radiation pattern. By reducing SLL, unwanted signals and interferences in the same frequency band are suppressed. PO is a novel global optimization method which is based on the multi-phased process of politics. The inter-element spacing and amplitude coefficients of concentric circular antenna array elements are determined optimally by PO algorithm to obtain a radiation pattern with a lower SLL value. To show the flexibility and performance of the proposed algorithm, three-ring CCAAs with different numbers of elements (4, 6, 8 elements and 8, 10, 12 elements) without and with the center element are synthesized. SLL values obtained by PO algorithm are compared with other recent optimization methods in the literature. The results obtained with PO algorithm in all comparisons show that it reached a lower SLL value than other optimization methods.