Antenna arrays play an increasingly important role in modern wireless communication systems. However, how to effectively suppress and optimize the side lobe level (SLL) of antenna arrays is critical for communication performance and communication capabilities. To solve the antenna array optimization problem, a new intelligent optimization algorithm called sparrow search algorithm (SSA) and its modification are applied to the electromagnetics and antenna community for the first time in this paper. Firstly, aimed at the shortcomings of SSA, such as being easy to fall into local optimum and limited convergence speed, a novel modified algorithm combining a homogeneous chaotic system, adaptive inertia weight, and improved boundary constraint is proposed. Secondly, three types of benchmark test functions are calculated to verify the effectiveness of the modified algorithm. Then, the element positions and excitation amplitudes of three different design examples of the linear antenna array (LAA) are optimized. The numerical results indicate that, compared with the other six algorithms, the modified algorithm has more advantages in terms of convergence accuracy, convergence speed, and stability, whether it is calculating the benchmark test functions or reducing the maximum SLL of the LAA. Finally, the electromagnetic (EM) simulation results obtained by FEKO also show that it can achieve a satisfactory beam pattern performance in practical arrays.

Antenna arrays with high directivity, low side-lobe level, and null control in desired direction and whip antenna with wider bandwidth both need to be optimized to meet different needs of communication systems. A new natural heuristic algorithm simulating social behavior of grasshoppers, grasshopper optimization algorithm (GOA), is applied to electromagnetic field as a new effective technology to solve the antenna optimization problem for the first time. Its algorithm is simple and has no gradient mechanism, can effectively avoid falling into local optimum, and is suitable for single-objective and multiobjective optimization problems. GOA is used to optimize the side lobe suppression, null depth, and notch control of arbitrary linear array and then used to optimize the loading and matching network of 10-meter HF broadband whip antenna compared with other algorithms. The results show that GOA has more advantages in side-lobe suppression, null depth, and notch control of linear array than other algorithms and has better broadband optimization performance for HF whip antenna. The pattern synthesis and antenna broadband optimization based on GOA provide a new and effective method for antenna performance optimization.

A novel invasive weed optimization (IWO) variant called chaotic adaptive invasive weed optimization (CAIWO) is proposed and applied for the optimization of nonuniform circular antenna arrays. A chaotic search method has been combined into the modified IWO with adaptive dispersion, where the seeds produced by a weed are dispersed in the search space with standard deviation specified by the fitness value of the weed. To evaluate the performance of CAIWO, several representative benchmark functions are minimized using various optimization algorithms. Numerical results demonstrate that the proposed approach improves the performance of the algorithm significantly, in terms of both the convergence speed and exploration ability. Moreover, the scheme of CAIWO is employed to find out an optimal set of weights and antenna element separation to obtain a radiation pattern with maximum side-lobe level (SLL) reduction with different numbers of antenna element under two cases with different purposes. The design results obtained by CAIWO have comfortably outperformed the published results obtained by other state-of-the-art metaheuristics in a statistically meaningful way.

Aiming at the problems of low gain, low efficiency at lower frequency, and warping in pattern at higher frequency of 10-meter high frequency (HF) whip antenna, the whip antenna is loaded and matched with the network in different bands using Grasshopper Optimization Algorithms (GOA) and antenna reconfiguration technology, so a new frequency reconfigurable broadband whip antenna is designed in this paper. According to the electrical characteristics of the 10-meter HF whip antenna, this paper divides short wave frequency into three bands and designs its radiation structure, loading, and matching network for each band of antenna, respectively. GOA is introduced into the research and design of antenna to optimize component parameters of the loading network and matching network. The results show that the antenna in lower frequency band can be improved at most, the maximum gain growth up to 5.8 dB (from −10.3 dB to −4.5 dB), and the maximum efficiency growth up to 8.5% (from 3% to 11.5%); the gain and efficiency in high frequency band are greatly improved too, and the phenomenon of warping in the pattern is effectively avoided.

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Planar antenna array design is one of the most important electromagnetic optimization problems of current interest. This paper introduces a recently developed metaheuristic algorithm, known as the Invasive Weed Optimization (IWO), to the pattern synthesis of planar antenna arrays with desired pattern nulls and sidelobe level by amplitude-only and position-only optimization. The steps in the problem formulation are presented along with a design example that illustrates the performance of the IWO algorithm. Three examples have been presented and solved. Simulation results are proposed to compare with published ones to verify the effectiveness of the IWO algorithm for planar arrays.

A novel chaotic adaptive butterfly mating optimization (CABMO) is proposed to be used in synthesizing the beam pattern. In order to improve the optimization accuracy and avoid trapping in the local optimum, the homogeneous chaotic system and adaptive movement mechanism are combined into the proposed algorithm, where the initialization and redistribution of butterflies are chaotically dispersed with an adaptive movement closely related to the ultraviolet changes. After validating the performance of CABMO through several benchmark functions with different dimensions, the improved algorithm outperforms when compared to other state-of-the-art nature-inspired metaheuristic algorithms. The proposed algorithm is then used to understand any linear array problems in terms of the sidelobe reduction. Finally, a CABMO strategy is utilized to optimize the mutual coupling model of the closely spaced VLF umbrella arrays. Results show that the optimized structure has comfortably outperformed the original structure. Full scanning of wave positions is realized from 15 to 30 kHz. The synthesis patterns are close to the theoretical optimum. The optimized results of the radiation performance and synthesized patterns demonstrate that the pattern synthesis and antenna structure optimization based on the CABMO algorithm provides a novel idea for antenna array optimization.

Abstract-As a very powerful optimization algorithm, invasive weed optimization has been widely applied to continuous optimization problems in electromagnetic (EM) field. However, the optimization of a thinned array can be formulated as a discrete-variable optimization problem with solutions encoded as binary strings. Therefore, in this paper, an improved binary invasive weed optimization (IBIWO) is proposed to design a thinned array with minimum side lobe levels. To evaluate the performance of the proposed algorithm, two examples have been presented and solved. Simulation results of the proposed thinned arrays obtained by IBIWO are compared with published results to verify the effectiveness of the proposed method.