An improved RIME optimization algorithm and its application in linear antenna arrays side lobe level suppression

This paper introduces an application of the dandelion optimization (DO) algorithm in antenna arrays. This is the first time that the DO algorithm has been used for optimizing antenna arrays. For antenna array optimization, sidelobe level (SLL) and deep nulls are key technical indicators. A lower SLL can improve the signal-to-noise ratio and reduce the impact of clutter signals outside the main beam. Deep nulls need to be aligned with the direction of interference to eliminate the influence of interference sources. The combination of the two can effectively improve the anti-interference ability of the entire system. Therefore, antenna arrays with ultra-low sidelobes and ultra-deep nulls are currently hot in the field of antenna array design and are also some of the key technologies needed to achieve modern high-performance radar systems. As a new type of evolutionary algorithm inspired by nature, the DO algorithm is inspired by the wind propagation behavior of dandelions in nature. This algorithm iteratively updates the population from three stages of ascent, descent, and landing, ultimately finding the optimal position. It has good optimization ability in solving complex problems such as those involving nonlinearity, discreteness, and non-convexity, and the antenna array pattern synthesis optimization problem belongs to multivariate nonlinear problems. Therefore, the DO algorithm can be effectively applied in the field of antenna array optimization. In this work, we use the following method to obtain an optimized pattern of a linear array with the lowest sidelobe level (SLL), null placement in particular directions, and a lower notch in particular directions: by controlling the antenna array’s element spacing and leaving the phase unchanged to optimize the current amplitudes and by controlling the excitation current and phase fixation of the antenna array and changing the element spacing. In the first and second examples, different algorithms are used to reduce the SLL of the antenna. In the first example, the DO algorithm reduces the SLL to −33.37 dB, which is 2.67 dB, 2.67 dB, 3.77 dB, 2.74 dB, and 2.52 dB lower than five other algorithms. In the second example, the SLL optimized by the DO algorithm is −42.56 dB, which is 5.04 dB and 1.48 dB lower than two other algorithms. In both examples, the DO algorithm reduces the SLL lower than other algorithms when the main lobe of the antenna is not significantly widened. Examples 3, 4, and 5 use the DO algorithm to optimize the amplitude of the current, generating deep nulls and deep notches in specific directions. In Example 3, the DO algorithm obtains a depth of nulls equal to −187.6 dB, which is 66.7 dB and 44.3 dB lower than that of the flower pollination algorithm (FPA) and the chaotic colony predation algorithm (CCPA), respectively. In Example 4, the deep null obtained by the DO algorithm is as low as −98.69 dB, which is 6.67 dB lower than the deep null obtained by the grey wolf optimization (GWO) algorithm. In Example 5, the deep notch obtained by the DO algorithm is as low as −63.1 dB, which is 6.4 dB and 1.9 dB lower than the spider monkey optimization (SMO) algorithm and the grasshopper optimization algorithm (GOA), respectively. The data prove that the DO algorithm produces deeper nulls and notches than other algorithms. The last two examples involve reducing sidelobe levels and generating deep nulls by optimizing the spacing between elements. In Example 5, the SLL obtained using the DO algorithm is −22.8766 dB, which is 0.1998 dB lower than the lowest SLL of −22.6768 dB among other algorithms. In Example 6, the SLL obtained using the DO algorithm is −20.1012 dB, and the null depth is −125.1 dB, which is 1.592 dB lower than the SLL obtained by the cat swarm optimization (CSO) algorithm and 19.1 dB lower than the deep null obtained by the GWO algorithm, respectively. In summary, the results of six simulation experiments indicate that the DO algorithm has better optimization ability in linear array optimization than other evolutionary algorithms.

A flexible search technique based on a tabu search algorithm is presented for the shaped‐beam pattern synthesis of linear antenna arrays. To synthesize a desired pattern, element excitations and/or positions are optimally determined. Examples of cosecant, flat‐topped, and pencil beam patterns are given to illustrate the achievable performance. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 36: 16–20, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10657

This paper presents an efficient method for the pattern synthesis of linear antenna array based on the differential evolution algorithm. A number of examples are presented to demonstrate the various capabilities of the this method for different number of elements. The main objective of this paper is to achieve a set of current weights in order to steer the nulls in undesired directions (nulling the undesired signals) and steering the main beam towards the desired direction(direction of desired signal) by changing the phase excitation currents. The fitness function is obtained from maximization of error between actual pattern and desired pattern.

This article details about the method of controlling Side-Lobe-Level (SLL) in the radiation pattern of mutually coupled symmetrical linear dipole antenna array in a proper direction. There are several conventional numerical and analytical methods to compensate the effect of mutual coupling, but to get better performance on linear dipole antenna arrays, an effective optimization technique is implemented; i.e.; Craziness based Particle Swarm Optimization (CRPSO). This technique provides solution for the nonlinear array factor equation which comprised of general n-dimensional components including current excitation of each antenna array element and inter-element spacing. In addition, analysis of comparative studies is discussed with the aid of calculating driving point impedance of antenna array for optimum inter-element spacing. An adequate amount of improvement in radiation pattern is achieved by reducing the unwanted SLL. Empirical study on the result is analyzed both quantitatively and qualitatively.

Pattern Search optimization with applications on synthesis of linear antenna arrays

Radiation pattern of linear arrays of microstrip rectangular antennas, on uniaxial anisotropic substrate is presented with Galerkin method. An optimization procedure based on the genetic algorithm (GA) method is introduced to reduce the side lobe level (SLL), by acting on the law of current amplitude of feed, spacing and phase of a linear array antennas. The results of optimisation are verified shows of narrow beamwidth are achieved.

Design of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of current interest. In this article, an adaptive Differential Evolution (DE) algorithm has been used to optimize the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. DE is arguably one of the best real parameter optimizers of current interest takes very few control parameters and is easy to implement in any programming language. In this study two very simple adaptation schemes are used to regulate the control parameters F and Cr, upon which the performance of DE is critically dependent. The adaptation schemes are based on the objective function values of the target vectors and donor vectors. The adaptive DE-variant has been used to solve three difficult instances of the design problem and the optimization goal in each example is easily achieved. The results of the proposed algorithm have been shown to meet or beat the recently published results obtained using other state-of-the-art metaheuristics like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Memetic Algorithms (MA), and Tabu Search (TS) in a statistically meaningful way.

Researches of dependency of aperture efficient of Dolph-Chebyshev antenna arrays on different lengths and random side-lobe levels are carried out in this work. Dependency of aperture efficiency on specified side-lobe level and on the length of antenna array is described. Analysis of the value of aperture efficiency for given side-lobe levels and the length of antenna array is done. Conclusions and recommendations for use of Dolph-Chebyshev amplitude distribution for linear antenna arrays with optimal length by the criterion of maximum aperture efficiency for given side-lobe levels according to the results are given in article. From the results we can see that for higher levels of side-lobes (more than − 30dB) for getting maximum possible value of aperture efficiency optimal length is highly dependent on side-lobe levels. This length is a few wavelengths. Optimal length of antenna array for side-lobe level less than −30dB is hundreds of wavelength. It is weakly dependent on side lobe levels.

This work is concerned with the methodology for improvement of the radiation pattern performance and eliminating the effect of unwanted interference of symmetric time modulated linear antenna array (STMLAA) in the predefined direction. This is achieved using evolutionary technique called Novel Particle Swarm Optimization with Wavelet Mutation (NPSOWM) for various application specific optimization like SLL only reduction, dual broad null and multiple broad null optimization. The optimized results obtained by using NPSOWM are validated by CST-MWS by captivating practical elements of dipole antenna and its array configuration. The numerical results show the effect of the particular (practical) antenna on the array. Various examples have taken for this purpose. A set of 16-element antenna array have designed for SLL only reduction. 16-, 32-, and 64-element STMLAA for incorporating the dual broad nulls in the angular range from 400 to 700 over the elevation plane. A set of 24-, 30-, and 32-element antenna array have occupied for multiple broad nulls. The strength of the adopted algorithm is tested for different number of array antenna configuration, which establish the efficacy of the optimization technique NPSOWM adopted for achieving the interference cancellation in the predefined direction and performance improvement of the antenna array such as simultaneous improvements of broad nulls (BNs), sidelobe level (SLL), sideband level (SBL), and the directivity. From the simulated results it is clear that NPSOWM can perform better than that of un-optimized linear antenna array with respect to BNs, SLL, and SBLs and Directivity.

A fast algorithm is given for computing the optimal weight vector in antenna array pattern synthesis having a set of null constraints. The algorithm requires only a UDUT decomposition of an m + 1 × m + 1 real positive definite matrix and the computation of the first row of the inverse of a triangular matrix of the same dimension, where m is the number of null constraints.

Antenna arrays are able to improve the directivity performance and reduce the cost of wireless communication systems. However, how to reduce the maximum sidelobe level (SLL) of the beam pattern is a key problem in antenna arrays. In this paper, three kinds of antenna arrays that are linear antenna array (LAA), circular antenna array (CAA) and random antenna array (RAA) are investigated. First, we formulate the SLL suppression optimization problems of LAA, CAA and RAA, respectively. Then, we propose a novel method called improved chicken swarm optimization (ICSO) approach to solve the formulated optimization problems. ICSO introduces four enhanced strategies including the local search factor, weighting factor and global search factor into the update method of conventional chicken swarm optimization (CSO) algorithm, respectively, for achieving better beam pattern optimization results of antenna arrays. Moreover, a variation mechanism is proposed to enhance the population diversity so that further improving the performance of the algorithm. We conduct simulations to evaluate the performance of the proposed ICSO for the maximum SLL suppressions of LAAs, CAAs and RAAs, and the results show that ICSO obtains lower maximum SLLs for different antenna array cases with different numbers of antenna elements compared to several other algorithms.

This article introduces an innovative approach to antenna array design, focusing on synthesizing the optimal radiation pattern for fifth-generation (5G) communication. The authors have designed a reliable linear and elliptical antenna array (EEA) of dipole elements by employing sailfish optimization (SFO). 5G technology promises transformative improvements in wireless communication with high data rates, expanded capacity, minimal latency, and exceptional service quality. The crux of 5G lies in the precision of antenna array design, aiming for an emission pattern with minimal side lobe levels (SLLs) and a narrow half-power beam width (HPBW). A narrower HPBW is essential for efficient long-range communication, whereas reducing the SLLs enhances signal clarity. The SFO optimizes the current excitation of each antenna element for reducing the mutual coupling effects and lowering the SLL and HPBW values in linear and EEAs. This paper uses the exact excitation to each element to show the linear antenna arrays (LAA) (10-, 16-element) design examples and EAA (8-, 12-, 20-element) structures. The LAA and EAA design examples obtained with the SFO algorithm establish the advancement in SLL suppression over the uniform antenna array and the methods proclaimed in the recent article.

Antenna arrays have become an essential part of most wireless communications systems. In this paper, the unwanted sidelobes in the symmetric linear array power pattern are reduced efficiently by utilizing a faster simultaneous sidelobes processing algorithm, which generates nulling sub-beams that are adapted to control and maintain steep convergence toward lower sidelobe levels. The proposed algorithm is performed using adaptive damping and heuristic factors which result in learning curve perturbations during the first few loops of the reduction process and is followed by a very steep convergence profile towards deep sidelobe levels. The numerical results show that, using the proposed adaptive sidelobes simultaneous reduction algorithm, a maximum sidelobe level of −50 dB can be achieved after only 10 iteration loops (especially for very large antenna arrays formed by 256 elements, wherein the processing time is reduced to approximately 25% of that required by the conventional fixed damping factor case). On the other hand, the generated array weights can be applied to practical linear antenna arrays under mutual coupling effects, which have shown very similar results to the radiation pattern of the isotropic antenna elements with very deep sidelobe levels and the same beamwidth.

Time modulated antenna arrays attracted the attention of researchers for the synthesis of low/ultra-low side lobes in recent past. In this article we propose an improved variant of a recently developed ecologically inspired metaheuristic, well-known as Invasive Weed Optimization (IWO), to solve the real parameter optimization problem related to the design of time-modulated linear antenna arrays with ultra low Side Lobe Level (SLL), Side Band Level (SBL) and Main Lobe Beam Width (BWFN). We improvise the classical IWO by introducing two parallel populations and a more explorative routine of changing the mutation step-size with iterations. Experimental results indicate that the proposed algorithm achieves better performance over the design problem as compared to the conventional Taylor Series based method and the only known metaheuristic approach based on the Differential Evolution (DE) algorithm.

An improved procedure to lessen side lobe levels (SLLs) and side band levels (SBLs) in time modulated linear antenna arrays (TMLAAs) by using the optimization process. In that optimization process we give the discrete values of static excitation amplitudes and phase of continuous values of on-time duration of the elements of antenna array. We used the particle swarm optimization (PSO) technique to reduce the maximum of SLLs and SBLs to a pre defined desired value of antenna element. This has been optimized in a way that the discrete values of input excitation amplitude are obtained by PSO algorithm. The simulation result of time-modulated linear antenna array (TMLAA) is presented by reducing SLL and SBLmax to below −40 dB and to −35 dB respectively by taking 30 elements.