Comparison of Evolutionary Optimization Algorithms for FM-TV Broadcasting Antenna Array Null Filling

Broadcasting antenna array null filling is a very challenging problem for antenna design optimization. This paper compares five antenna design optimization algorithms (Differential Evolution, Particle Swarm, Taguchi, Invasive Weed, Adaptive Invasive Weed) as solutions to the antenna array null filling problem. The algorithms compared are evolutionary algorithms which use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. The focus of the comparison is given to the algorithm with the best results, nevertheless, it becomes obvious that the algorithm which produces the best fitness (Invasive Weed Optimization) requires very substantial computational resources due to its random search nature

Antenna Array Pattern Synthesis via Coordinate Descent Method

This paper presents an array pattern synthesis algorithm for arbitrary arrays based on coordinate descent method (CDM). With this algorithm, the complex element weights are found to minimize a weighted L2 norm of the difference between desired and achieved pattern. Compared with traditional optimization techniques, CDM is easy to implement and efficient to reach the optimum solutions. Main advantage is the flexibility. CDM is suitable for linear and planar array with arbitrary array elements on arbitrary positions. With this method, we can configure arbitrary beam pattern, which gives it the ability to solve variety of beam forming problem, e.g. focused beam, shaped beam, nulls at arbitrary direction and with arbitrary beam width. CDM is applicable for phase-only and amplitude-only arrays as well, and furthermore, it is a suitable method to treat the problem of array with element failures

Optimisation of DTV coverage and broadcasting antennas

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe increased use of the available radio frequency spectrum by many existing as well as new technologies brings up the need of more advanced and specialised antennas, designed for very specific purposes. A very good example is the withdrawal of analogue TV from the radio spectrum, which has given space to be used by newer technologies such as 4G and 5G. Optimising the design of an antenna for a specific purpose is a goal that becomes more and more necessary. This suggests that an investigation of methods to optimise an electromagnetic design with the best possible results at the best possible time is also necessary. Evolutionary Algorithms (EA) are a very well know method which exhibits solid results within a smallest possible time for electromagnetic problems (e.g. antenna design optimisation). EA are nature inspired algorithms, and some very popular examples, which are widely used in antenna optimisation are the Differential Evolution (DE), Particle Swarm Optimisation (PSO) and Invasive Weed Optimisation (IWO). This thesis researches a comparison between the aforementioned methods, while also proposing a novel method, which is a modified version of IWO and has proven to be very solid. To determine the efficacy of the proposed method, all the algorithms were compared on some of the most major test functions for such purposes. Some examples are Ackley’s, De Jong’s, Holder table, Rastrigin and Rosenbrock. By employing these test functions, it was possible to determine the optimum settings of the modified IWO version. These methods are compared for different antenna design optimisation simulations using software such as MATLAB and CST Microwave Studio, to determine which method yields the best results and to output novel optimised antenna designs for different purposes. Some of the novel antenna designs that were applied to EAs for optimisation are a collinear dipole array with a specifically shaped radiation pattern and a log-periodic dipole antenna (LPDA) with flat gain response across its operating spectrum for Digital TV (DTV) broadcasting purposes. Other novel designs include a planar elliptical dipole antenna for Ultra-Wideband (UWB) Electromagnetic Compatibility (EMC) applications such as EMC measurements and a pin-fed notched circular patch antenna with circular polarisation for satellite communications, in which cases the small size of the generated geometries was also a goal so that portability is achieved. The geometrical parameters of the best possible antenna design were in some cases fabricated and compared to the simulated results so that the latter is compared to real world applications

Conflicting Parameter Pair Optimization for Linear Aperiodic Antenna Array using Chebyshev Taper based Genetic Algorithm

In this study, the peak side lobe level (PSLL) in the radiation pattern of a linear antenna array (LAA) is lowered without affecting its first null beam width (FNBW). Antenna array synthesis is commonly applied to achieve high directivity, low side lobes, high gain and desired null positions in the output radiation pattern. But output parameters like PSLL, null positions and beam width conflict with each other, i.e. as one parameter improves, the other deteriorates. To avoid this problem, a multi-objective optimization algorithm can be implemented, in which both the conflicting parameters can be simultaneously optimized. This work proposes a multi-objective algorithm, which takes advantages of the well-known Chebyshev tapering and genetic algorithm (GA), to lower the PSLL without broadening the beam further. Array elements are fed using Chebyshev tapered excitations while GA is incorporated to optimize the elemental spacing. The results of 28-element LAA are compared with those of multi-objective Cauchy mutated cat swarm optimization (MO-CMCSO) existing in literature, which has also been proven to be superior to multi-objective cat swarm optimization (MO-CSO) and multi-objective particle swarm optimization (MO-PSO). Results indicate that the proposed algorithm performs better by further reducing the PSLL from -21.57 dB (MO-CMCSO) to -28.18 dB, while maintaining the same FNBW of 7.4 degrees

Pattern Synthesis of Planar Nonuniform Circular Antenna Arrays Using a Chaotic Adaptive Invasive Weed Optimization Algorithm

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

Comparative and comprehensive study of linear antenna arrays’ synthesis

In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), antlion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm

Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program

Low sidelobe synthesis of dipole arrays by element orientation selection using binary codec genetic algorithm

© 2017 Euraap. Selecting appropriate element orientations can significantly reduce the sidelobe level of the antenna array. In this paper, a binary coded genetic algorithm (BCGA) which selects the element orientations from specified discrete angles, is proposed to reduce the sidelobe level (SLL) of the array. Compared to the conventional GA, the BCGA is much faster in this application. Synthesis results show the effectiveness and efficiency of the proposed method

Comparative Analysis of Linear and Nonlinear Pattern Synthesis of Hemispherical Antenna Array Using Adaptive Evolutionary Techniques

Hemispherical antenna arrays are subjected to linear and nonlinear synthesis and are optimized using adaptive based differential evolution (ADE) and fire fly (AFA) algorithm. The hemispherical shaped array with isotropic elements is considered. Antenna element parameters that are used for synthesis are excitation amplitude and angular position. Linear synthesis is termed as the variation in the element excitation amplitude and nonlinear synthesis is process of variation in element angular position. Both ADE and AFA are a high-performance stochastic evolutionary algorithm used to solve N-dimensional problems. These methods are used to determine a set of parameters of antenna elements that provide the desired radiation pattern. The effectiveness of the algorithms for the design of conformal antenna array is shown by means of numerical results. Comparison with other methods is made whenever possible. The results reveal that nonlinear synthesis, aided by the discussed techniques, provides considerable enhancements compared to linear synthesis