The synthesis of antenna arrays with low sidelobe levels is needed to enhance the communication systems’ efficiency. In this paper, new arbitrary geometries that improve the ability of the antenna arrays to minimize the sidelobe level, are proposed. We employ the well-known superformula equation in the antenna arrays field by implementing the equation in the general array factor equation. Three metaheuristic optimization algorithms are used to synthesize the antenna arrays and their geometries; antlion optimization (ALO) algorithm, grasshopper optimization algorithm (GOA), and a new hybrid algorithm based on ALO and GOA. All the proposed algorithms are high-performance computational methods, which proved their efficiency for solving different real-world optimization problems. 15 design examples are presented and compared to prove validity with the most general standard geometry: elliptical antenna array (EAA). It is observed that the proposed geometries outperform EAA geometries by 4.5 dB and 10.9 dB in the worst and best scenarios, respectively, which proves the advantage and superiority of our approach.
Comparative and comprehensive study of linear antenna arrays’ synthesisIJECEIAES
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), ant lion 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.
The optimal synthesis of scanned linear antenna arrays IJECEIAES
In this paper, symmetric scanned linear antenna arrays are synthesized, in order to minimize the side lobe level of the radiation pattern. The feeding current amplitudes are considered as the optimization parameters. Newly proposed optimization algorithms are presented to achieve our target; Antlion Optimization (ALO) and a new hybrid algorithm. Three different examples are illustrated in this paper; 20, 26 and 30 elements scanned linear antenna array. The obtained results prove the effectiveness and the ability of the proposed algorithms to outperform and compete other algorithms like Symbiotic Organisms Search (SOS) and Firefly Algorithm (FA).
In the recent years the development in communication systems requires the development of low cost, minimal weight, low profile antennas that are capable of maintaining high performance over a wide spectrum of frequencies. This technological trend has focused much effort into the design of a micro strip patch antennas. Nowadays Evolutionary Computation has its growth to extent. Generally electromagnetic optimization problems generally involve a large number of parameters. Synthesis of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of the current interest.
Synthesis of a Sparse 2D-Scanning Array using Particle Swarm Optimization for...Sivaranjan Goswami
The document describes a method for synthesizing a sparse 2D phased array antenna using particle swarm optimization to reduce side lobes. A 16x16 uniform rectangular array is used as a starting point. An artificial neural network is trained to map the excitation phase shifts to the main radiation angles. Particle swarm optimization is then used to determine a sparse array configuration and excitation weights that maintain low side lobe levels while removing half the elements. The results show the synthesized sparse array achieves similar side lobe performance to the original uniform array.
Economic Load Dispatch Problem with Valve – Point Effect Using a Binary Bat A...IDES Editor
This paper proposes application of BAT algorithm
for solving economic load dispatch problem. BAT
algorithmic rule is predicated on the localization
characteristics of micro bats. The proposed approach has
been examined and tested with the numerical results of
economic load dispatch problems with three and five
generating units with valve - point loading without
considering prohibited operating zones and ramp rate limits.
The results of the projected BAT formula are compared with
that of other techniques such as lambda iteration, GA, PSO,
APSO, EP, ABC and basic principle. For each case, the
projected algorithmic program outperforms the answer
reported for the existing algorithms. Additionally, the
promising results show the hardness, quick convergence
and potency of the projected technique.
The document summarizes research on thinning semi-elliptical and quarter-elliptical antenna arrays using genetic algorithm optimization to reduce side-lobe levels. Key points:
1) Genetic algorithms were used to optimize thinning of semi-elliptical and quarter-elliptical arrays with uniform excitation and spacing to produce narrow beams without degrading performance.
2) Simulation results showed thinning using genetic algorithms reduced side-lobe levels of the arrays compared to fully populated arrays.
3) Maximum side-lobe levels, beamwidths, and other metrics were tabulated for various array sizes and geometries, indicating thinning effectively lowered side-lobes.
OPTIMIZATION OF COST 231 MODEL FOR 3G WIRELESS COMMUNICATION SIGNAL IN SUBURB...Onyebuchi nosiri
This document describes the optimization of the Cost 231 model for 3G wireless communication signals in a suburban area of Port Harcourt, Nigeria. Field measurements were taken using drive testing equipment. The least squares algorithm was used to optimize parameters in the Cost 231 model based on the measured data. This reduced the mean absolute deviation from 1.196 to 1.179 and the mean squared deviation from 2.01 to 1.94, showing better prediction accuracy of the optimized model compared to the original Cost 231 model for the environment.
OPTIMIZATION OF COST 231 MODEL FOR 3G WIRELESS COMMUNICATION SIGNAL IN SUBURB...Onyebuchi nosiri
This document describes the optimization of the Cost 231 model for 3G wireless communication signals in a suburban area of Port Harcourt, Nigeria. Field measurements were taken using drive testing equipment. The least squares algorithm was used to optimize parameters in the Cost 231 model based on the measured data. This reduced the mean absolute deviation from 1.196 to 1.179 and the mean squared deviation from 2.01 to 1.94, showing better prediction accuracy of the optimized model compared to the original Cost 231 model for the environment.
Comparative and comprehensive study of linear antenna arrays’ synthesisIJECEIAES
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), ant lion 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.
The optimal synthesis of scanned linear antenna arrays IJECEIAES
In this paper, symmetric scanned linear antenna arrays are synthesized, in order to minimize the side lobe level of the radiation pattern. The feeding current amplitudes are considered as the optimization parameters. Newly proposed optimization algorithms are presented to achieve our target; Antlion Optimization (ALO) and a new hybrid algorithm. Three different examples are illustrated in this paper; 20, 26 and 30 elements scanned linear antenna array. The obtained results prove the effectiveness and the ability of the proposed algorithms to outperform and compete other algorithms like Symbiotic Organisms Search (SOS) and Firefly Algorithm (FA).
In the recent years the development in communication systems requires the development of low cost, minimal weight, low profile antennas that are capable of maintaining high performance over a wide spectrum of frequencies. This technological trend has focused much effort into the design of a micro strip patch antennas. Nowadays Evolutionary Computation has its growth to extent. Generally electromagnetic optimization problems generally involve a large number of parameters. Synthesis of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of the current interest.
Synthesis of a Sparse 2D-Scanning Array using Particle Swarm Optimization for...Sivaranjan Goswami
The document describes a method for synthesizing a sparse 2D phased array antenna using particle swarm optimization to reduce side lobes. A 16x16 uniform rectangular array is used as a starting point. An artificial neural network is trained to map the excitation phase shifts to the main radiation angles. Particle swarm optimization is then used to determine a sparse array configuration and excitation weights that maintain low side lobe levels while removing half the elements. The results show the synthesized sparse array achieves similar side lobe performance to the original uniform array.
Economic Load Dispatch Problem with Valve – Point Effect Using a Binary Bat A...IDES Editor
This paper proposes application of BAT algorithm
for solving economic load dispatch problem. BAT
algorithmic rule is predicated on the localization
characteristics of micro bats. The proposed approach has
been examined and tested with the numerical results of
economic load dispatch problems with three and five
generating units with valve - point loading without
considering prohibited operating zones and ramp rate limits.
The results of the projected BAT formula are compared with
that of other techniques such as lambda iteration, GA, PSO,
APSO, EP, ABC and basic principle. For each case, the
projected algorithmic program outperforms the answer
reported for the existing algorithms. Additionally, the
promising results show the hardness, quick convergence
and potency of the projected technique.
The document summarizes research on thinning semi-elliptical and quarter-elliptical antenna arrays using genetic algorithm optimization to reduce side-lobe levels. Key points:
1) Genetic algorithms were used to optimize thinning of semi-elliptical and quarter-elliptical arrays with uniform excitation and spacing to produce narrow beams without degrading performance.
2) Simulation results showed thinning using genetic algorithms reduced side-lobe levels of the arrays compared to fully populated arrays.
3) Maximum side-lobe levels, beamwidths, and other metrics were tabulated for various array sizes and geometries, indicating thinning effectively lowered side-lobes.
OPTIMIZATION OF COST 231 MODEL FOR 3G WIRELESS COMMUNICATION SIGNAL IN SUBURB...Onyebuchi nosiri
This document describes the optimization of the Cost 231 model for 3G wireless communication signals in a suburban area of Port Harcourt, Nigeria. Field measurements were taken using drive testing equipment. The least squares algorithm was used to optimize parameters in the Cost 231 model based on the measured data. This reduced the mean absolute deviation from 1.196 to 1.179 and the mean squared deviation from 2.01 to 1.94, showing better prediction accuracy of the optimized model compared to the original Cost 231 model for the environment.
OPTIMIZATION OF COST 231 MODEL FOR 3G WIRELESS COMMUNICATION SIGNAL IN SUBURB...Onyebuchi nosiri
This document describes the optimization of the Cost 231 model for 3G wireless communication signals in a suburban area of Port Harcourt, Nigeria. Field measurements were taken using drive testing equipment. The least squares algorithm was used to optimize parameters in the Cost 231 model based on the measured data. This reduced the mean absolute deviation from 1.196 to 1.179 and the mean squared deviation from 2.01 to 1.94, showing better prediction accuracy of the optimized model compared to the original Cost 231 model for the environment.
For high performance communication systems, Side Lobe Level (SLL) reduction and improved directivity are the goal of antenna designers. In the recent years, many optimization techniques of antenna design are occupying demanding place over the analytical techniques. Though they have contributed attractive solutions, it is often obvious to select one that meets the particular design need at hand. In this paper, an optimization technique called Self-adaptive Differential Evolution (SaDE) that can be able to learn and behave intelligently along with hyper beam forming is integrated to determine an optimal set of excitation weights in the design of EcAA. Non-uniform excitation weights of the individual array elements of EcAA are performed to obtain reduced SLL, high directivity and flexible radiation pattern. To evaluate the improved performance of the proposed SaDE optimized hyper beam, comparison are done with uniformly excited, SaDE without hyper beam and Genetic Algorithm (GA). In general, the proposed work of pattern synthesis has resulted in much better reduction of SLL and FNBW than both the uniformly excited and thinned EcAA. The results of this study clearly reveal that the SLL highly reduced at a very directive beamwidth.
This paper proposes a parameterized model order reduction technique for efficient global sensitivity analysis of coupled coils over a design space. It uses parameterized models of the electromagnetic matrices and Krylov matrices from the original and adjoint systems, derived using interpolation. Numerical results confirm the efficiency and accuracy of the proposed method for sensitivity analysis across the full design parameter space.
This paper proposes a parameterized model order reduction technique for efficient global sensitivity analysis of coupled coils over a design space. It uses parameterized models of the electromagnetic matrices and Krylov matrices from the original and adjoint systems, derived using interpolation. Numerical results confirm the efficiency and accuracy of the proposed method for sensitivity analysis across the entire design space of interest.
This paper presents a parameterized model order reduction technique for efficient global sensitivity analysis of coupled coils. It uses parameterized models of the electromagnetic matrices and Krylov matrices from the original and adjoint systems computed using PEEC. Numerical results on a model of two coupled coils confirm the accuracy and efficiency of the proposed method compared to existing techniques for sensitivity analysis over the entire design space.
IRJET- Optimal Placement and Size of DG and DER for Minimizing Power Loss and...IRJET Journal
This document presents a study on using optimization techniques to determine the optimal placement and sizing of distributed generation (DG) and distributed energy resources (DER) in a 69-bus distribution system. The study uses two optimization algorithms - Grasshopper Optimization Algorithm (GOA) and Moth Flame Optimization (MFO) - to minimize power losses and annual energy losses at different load levels. The results show that MFO performs better, identifying bus locations 61, 11, and 18 as optimal for DG placement, reducing losses more than GOA. For DER placement using MFO, losses are minimized by placing DG at buses 69, 61, 22 and capacitors at buses 61, 49, 12. Overall, the
The document describes modeling the 3D geometry of relativistic jets based on 2D projection observations. It presents a mathematical model that uses Bayesian parameter estimation via MCMC to statistically estimate the 3D structural parameters of jet bends from measured 2D projection properties. Figures and equations show how the model accounts for projection effects to determine the intrinsic 3D geometry from observed 2D jet features like bend angles and distances.
IRJET- Optimal Placement and Size of DG and DER for Minimizing Power Loss and...IRJET Journal
This document summarizes research on optimizing the placement and sizing of distributed generation (DG) and distributed energy resources (DER) in a 33-bus distribution system to minimize power losses. Two optimization techniques are evaluated: Grasshopper Optimization Algorithm (GOA) and Moth Flame Optimization (MFO). MFO shows better results, identifying bus 13, 24 and 30 as optimal locations for DG, reducing losses from 0.2027 MW to 0.0715 MW at normal load. For DER, optimal locations are DG at buses 13, 25, 30 and capacitors at buses 7, 13, 30, further reducing losses to 0.0144 MW. Graphs and tables show MFO placement
Enhancing radial distribution system performance by optimal placement of DST...IJECEIAES
In this paper, A novel modified optimization method was used to find the optimal location and size for placing distribution Static Compensator in the radial distribution test feeder in order to improve its performance by minimizing the total power losses of the test feeder, enhancing the voltage profile and reducing the costs. The modified grey wolf optimization algorithm is used for the first time to solve this kind of optimization problem. An objective function was developed to study the radial distribution system included total power loss of the system and costs due to power loss in system. The proposed method is applied to two different test distribution feeders (33 bus and 69 bus test systems) using different Dstatcom sizes and the acquired results were analyzed and compared to other recent optimization methods applied to the same test feeders to ensure the effectiveness of the used method and its superiority over other recent optimization mehods. The major findings from obtained results that the applied technique found the most minimized total power loss in system, the best improved voltage profile and most reduction in costs due power loss compared to other methods.
This academic article discusses numerical flow simulation of an elbow draft tube using STAR-CCM+ software. It summarizes the steps taken which include geometric modeling of the draft tube, mesh generation, specification of parameters like material properties and boundary conditions, and setting up the implicit unsteady simulation. Results of the simulation like pressure and velocity variations along the draft tube over different time steps are presented. Key findings are that pressure and velocity distributions are affected by time step size, with smaller time steps showing more uniform distributions. Performance metrics like head recovery and efficiency of the draft tube are computed and seen to decrease initially with time step before leveling off.
This paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
Multi objective economic load dispatch using hybrid fuzzy, bacterialIAEME Publication
The document summarizes a research paper that proposes a new approach for solving the economic load dispatch problem using a hybrid fuzzy, bacterial foraging-Nelder–Mead algorithm. The economic load dispatch problem minimizes generation costs while satisfying load demand under system constraints. The proposed approach considers generation costs, spinning reserve costs, and emission costs simultaneously. It also accounts for valve-point effects, prohibited operating zones, and other practical constraints. A hybrid bacterial foraging and Nelder–Mead algorithm combined with fuzzy logic is used to solve the optimization problem. Simulation results show the advantages of the proposed method in reducing total system costs.
Capacitor Placement and Reconfiguration of Distribution System with hybrid Fu...IOSR Journals
The document describes a hybrid fuzzy-opposition based differential evolution algorithm for capacitor placement and distribution system reconfiguration to minimize transmission losses and costs. The algorithm considers constraints like voltage limits and current limits while optimizing the objective function of total annual cost, which includes energy loss costs and capacitor costs. It was tested on the IEEE 33-bus distribution test system and able to reduce losses and satisfy power flow constraints.
This document summarizes research on using particle swarm optimization to reconstruct microwave images of two-dimensional dielectric scatterers. It formulates the inverse scattering problem as an optimization problem to find the dielectric parameter distribution that minimizes the difference between measured and simulated scattered field data. Numerical results show that a particle swarm optimization approach can accurately reconstruct the shape and dielectric properties of a test cylindrical scatterer, with lower background reconstruction error than a genetic algorithm approach. The research demonstrates that particle swarm optimization is a suitable technique for high-dimensional microwave imaging problems.
GWO-based estimation of input-output parameters of thermal power plantsTELKOMNIKA JOURNAL
This document presents a study that uses the Grey Wolf Optimizer (GWO) method to estimate the input-output parameters of the fuel cost curve for thermal power plants.
The fuel cost curve represents the relationship between a plant's fuel costs and power output, and needs to be periodically re-estimated due to temperature and aging effects. Accurately estimating the curve's parameters is important for economic dispatch calculations.
The study formulates parameter estimation as an optimization problem to minimize errors between actual and estimated fuel costs. It applies GWO to find the parameters for different fuel cost curve models using test data from three power plants. Simulation results show GWO provides better parameter estimates than other estimation methods.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
OPTIMIZATION OF AERODYNAMIC AND STRUCTURAL PERFORMANCES OF A WIND TURBINE BLA...IAEME Publication
The purpose of this study is to optimize the energy efficiency of a wind turbine
blade and reduce its cost. In this paper, we define several optimization targets such as
maximizing Cl / Cd ratio and minimizing the deformation and mass of the blade. To
solve this multi-objectives optimization problem, we used the ant colony heuristic
optimization method on a blade model computed by the BEM and the FEM methods.
The optimization results are compared with the results obtained by the BEM method.
- The document discusses using a modified genetic algorithm to optimize parameters for automatic generation control of multi-area electric energy systems.
- It proposes a modified genetic algorithm that employs one-point crossover with modification. The crossover site helps maintain diversity of search points while exploiting known optimal values, balancing exploration and exploitation.
- The algorithm is used along with decomposition techniques and trapezoidal integration to determine optimal control parameters for multi-area systems and obtain better dynamic performance under small load perturbations, with and without considering nonlinearity from governor deadbands.
Improving the Hydraulic Efficiency of Centrifugal Pumps through Computational...IJERA Editor
The design and optimization of turbo machine impellers such as those in pumps and turbines is a highly complicated task due to the complex three-dimensional shape of the impeller blades and surrounding devices. Small differences in geometry can lead to significant changes in the performance of these machines. We report here an efficient numerical technique that automatically optimizes the geometry of these blades for maximum performance. The technique combines, mathematical modeling of the impeller blades using non-uniform rational B-spline (NURBS), Computational fluid dynamics (CFD) with Geometry Parameterizations in turbulent flow simulation and the Globalized and bounded Nelder-Mead (GBNM) algorithm in geometry optimization.
Rabid Euclidean direction search algorithm for various adaptive array geometriesjournalBEEI
This document summarizes the Rabid Euclidean direction search (REDS) algorithm for adaptive beamforming in different antenna array geometries. The REDS algorithm is analyzed for linear, circular, and planar array configurations. Simulation results show that the REDS algorithm provides significant improvements over other algorithms, including faster convergence, lower mean square error, better interference reduction, and lower sidelobe levels. Specifically, the REDS algorithm cyclically searches and updates weights along individual Euclidean directions to minimize error, offering computational advantages over algorithms like least mean square and recursive least squares.
Phase weighting of shaped pattern for base station antenna arrays marwaeng
The document discusses phase weighting techniques for synthesizing shaped radiation patterns in base station antenna arrays. It presents an algorithm that uses genetic optimization and particle swarm optimization to determine the phase weights needed to generate a desired shaped pattern. This is achieved by first using statistical sampling to get an initial approximation, then refining it with the optimization algorithms. Examples are given showing shaped patterns produced that meet requirements like low sidelobe levels and filled nulls. Phase-only weighting is preferred over amplitude weighting because it is easier to implement physically in the antenna feed network.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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For high performance communication systems, Side Lobe Level (SLL) reduction and improved directivity are the goal of antenna designers. In the recent years, many optimization techniques of antenna design are occupying demanding place over the analytical techniques. Though they have contributed attractive solutions, it is often obvious to select one that meets the particular design need at hand. In this paper, an optimization technique called Self-adaptive Differential Evolution (SaDE) that can be able to learn and behave intelligently along with hyper beam forming is integrated to determine an optimal set of excitation weights in the design of EcAA. Non-uniform excitation weights of the individual array elements of EcAA are performed to obtain reduced SLL, high directivity and flexible radiation pattern. To evaluate the improved performance of the proposed SaDE optimized hyper beam, comparison are done with uniformly excited, SaDE without hyper beam and Genetic Algorithm (GA). In general, the proposed work of pattern synthesis has resulted in much better reduction of SLL and FNBW than both the uniformly excited and thinned EcAA. The results of this study clearly reveal that the SLL highly reduced at a very directive beamwidth.
This paper proposes a parameterized model order reduction technique for efficient global sensitivity analysis of coupled coils over a design space. It uses parameterized models of the electromagnetic matrices and Krylov matrices from the original and adjoint systems, derived using interpolation. Numerical results confirm the efficiency and accuracy of the proposed method for sensitivity analysis across the full design parameter space.
This paper proposes a parameterized model order reduction technique for efficient global sensitivity analysis of coupled coils over a design space. It uses parameterized models of the electromagnetic matrices and Krylov matrices from the original and adjoint systems, derived using interpolation. Numerical results confirm the efficiency and accuracy of the proposed method for sensitivity analysis across the entire design space of interest.
This paper presents a parameterized model order reduction technique for efficient global sensitivity analysis of coupled coils. It uses parameterized models of the electromagnetic matrices and Krylov matrices from the original and adjoint systems computed using PEEC. Numerical results on a model of two coupled coils confirm the accuracy and efficiency of the proposed method compared to existing techniques for sensitivity analysis over the entire design space.
IRJET- Optimal Placement and Size of DG and DER for Minimizing Power Loss and...IRJET Journal
This document presents a study on using optimization techniques to determine the optimal placement and sizing of distributed generation (DG) and distributed energy resources (DER) in a 69-bus distribution system. The study uses two optimization algorithms - Grasshopper Optimization Algorithm (GOA) and Moth Flame Optimization (MFO) - to minimize power losses and annual energy losses at different load levels. The results show that MFO performs better, identifying bus locations 61, 11, and 18 as optimal for DG placement, reducing losses more than GOA. For DER placement using MFO, losses are minimized by placing DG at buses 69, 61, 22 and capacitors at buses 61, 49, 12. Overall, the
The document describes modeling the 3D geometry of relativistic jets based on 2D projection observations. It presents a mathematical model that uses Bayesian parameter estimation via MCMC to statistically estimate the 3D structural parameters of jet bends from measured 2D projection properties. Figures and equations show how the model accounts for projection effects to determine the intrinsic 3D geometry from observed 2D jet features like bend angles and distances.
IRJET- Optimal Placement and Size of DG and DER for Minimizing Power Loss and...IRJET Journal
This document summarizes research on optimizing the placement and sizing of distributed generation (DG) and distributed energy resources (DER) in a 33-bus distribution system to minimize power losses. Two optimization techniques are evaluated: Grasshopper Optimization Algorithm (GOA) and Moth Flame Optimization (MFO). MFO shows better results, identifying bus 13, 24 and 30 as optimal locations for DG, reducing losses from 0.2027 MW to 0.0715 MW at normal load. For DER, optimal locations are DG at buses 13, 25, 30 and capacitors at buses 7, 13, 30, further reducing losses to 0.0144 MW. Graphs and tables show MFO placement
Enhancing radial distribution system performance by optimal placement of DST...IJECEIAES
In this paper, A novel modified optimization method was used to find the optimal location and size for placing distribution Static Compensator in the radial distribution test feeder in order to improve its performance by minimizing the total power losses of the test feeder, enhancing the voltage profile and reducing the costs. The modified grey wolf optimization algorithm is used for the first time to solve this kind of optimization problem. An objective function was developed to study the radial distribution system included total power loss of the system and costs due to power loss in system. The proposed method is applied to two different test distribution feeders (33 bus and 69 bus test systems) using different Dstatcom sizes and the acquired results were analyzed and compared to other recent optimization methods applied to the same test feeders to ensure the effectiveness of the used method and its superiority over other recent optimization mehods. The major findings from obtained results that the applied technique found the most minimized total power loss in system, the best improved voltage profile and most reduction in costs due power loss compared to other methods.
This academic article discusses numerical flow simulation of an elbow draft tube using STAR-CCM+ software. It summarizes the steps taken which include geometric modeling of the draft tube, mesh generation, specification of parameters like material properties and boundary conditions, and setting up the implicit unsteady simulation. Results of the simulation like pressure and velocity variations along the draft tube over different time steps are presented. Key findings are that pressure and velocity distributions are affected by time step size, with smaller time steps showing more uniform distributions. Performance metrics like head recovery and efficiency of the draft tube are computed and seen to decrease initially with time step before leveling off.
This paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
Multi objective economic load dispatch using hybrid fuzzy, bacterialIAEME Publication
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Capacitor Placement and Reconfiguration of Distribution System with hybrid Fu...IOSR Journals
The document describes a hybrid fuzzy-opposition based differential evolution algorithm for capacitor placement and distribution system reconfiguration to minimize transmission losses and costs. The algorithm considers constraints like voltage limits and current limits while optimizing the objective function of total annual cost, which includes energy loss costs and capacitor costs. It was tested on the IEEE 33-bus distribution test system and able to reduce losses and satisfy power flow constraints.
This document summarizes research on using particle swarm optimization to reconstruct microwave images of two-dimensional dielectric scatterers. It formulates the inverse scattering problem as an optimization problem to find the dielectric parameter distribution that minimizes the difference between measured and simulated scattered field data. Numerical results show that a particle swarm optimization approach can accurately reconstruct the shape and dielectric properties of a test cylindrical scatterer, with lower background reconstruction error than a genetic algorithm approach. The research demonstrates that particle swarm optimization is a suitable technique for high-dimensional microwave imaging problems.
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Improving the Hydraulic Efficiency of Centrifugal Pumps through Computational...IJERA Editor
The design and optimization of turbo machine impellers such as those in pumps and turbines is a highly complicated task due to the complex three-dimensional shape of the impeller blades and surrounding devices. Small differences in geometry can lead to significant changes in the performance of these machines. We report here an efficient numerical technique that automatically optimizes the geometry of these blades for maximum performance. The technique combines, mathematical modeling of the impeller blades using non-uniform rational B-spline (NURBS), Computational fluid dynamics (CFD) with Geometry Parameterizations in turbulent flow simulation and the Globalized and bounded Nelder-Mead (GBNM) algorithm in geometry optimization.
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Phase weighting of shaped pattern for base station antenna arrays marwaeng
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Similar to Synthesis of new antenna arrays with arbitrary geometries based on the superformula (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
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the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
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our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
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Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
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Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
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An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
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A review on features and methods of potential fishing zoneIJECEIAES
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Electrical signal interference minimization using appropriate core material f...IJECEIAES
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Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
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the past ten years, women's involvement in society has grown dramatically,
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Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
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Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
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Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
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Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
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techniques based on signal processing and computational intelligence.
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factors, including implementation costs, non-detected zones, declining
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(AHP). The multi-criteria decision-making analysis shows that the overall
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comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
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solar cells' peak output. In this study, a single-stage grid-connected PV
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logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
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reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
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atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
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Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
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Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
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Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
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Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
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An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
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Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Synthesis of new antenna arrays with arbitrary geometries based on the superformula
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 6, December 2022, pp. 6228~6238
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i6.pp6228-6238 6228
Journal homepage: http://ijece.iaescore.com
Synthesis of new antenna arrays with arbitrary geometries
based on the superformula
Anas A. Amaireh1
, Nihad I. Dib2
, Asem S. Al-Zoubi3
1
Electrical and Computer Engineering Department, The University of Oklahoma, Norman, United State
2
Electrical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
3
Telecommunications Engineering Department, Yarmouk University, Irbid, Jordan
Article Info ABSTRACT
Article history:
Received Sep 20, 2021
Revised Jun 7, 2022
Accepted Jul 5, 2022
The synthesis of antenna arrays with low sidelobe levels is needed to
enhance the communication systems’ efficiency. In this paper, new arbitrary
geometries that improve the ability of the antenna arrays to minimize the
sidelobe level, are proposed. We employ the well-known superformula
equation in the antenna arrays field by implementing the equation in the
general array factor equation. Three metaheuristic optimization algorithms
are used to synthesize the antenna arrays and their geometries; antlion
optimization (ALO) algorithm, grasshopper optimization algorithm (GOA),
and a new hybrid algorithm based on ALO and GOA. All the proposed
algorithms are high-performance computational methods, which proved their
efficiency for solving different real-world optimization problems. 15 design
examples are presented and compared to prove validity with the most
general standard geometry: elliptical antenna array (EAA). It is observed
that the proposed geometries outperform EAA geometries by 4.5 dB and
10.9 dB in the worst and best scenarios, respectively, which proves the
advantage and superiority of our approach.
Keywords:
Antenna arrays
Antlion optimization algorithm
Grasshopper optimization
algorithm
Metaheuristics algorithms
Superformula equation
This is an open access article under the CC BY-SA license.
Corresponding Author:
Anas A. Amaireh
Electrical and Computer Engineering Department, The University of Oklahoma
Norman 73019, United State
Email: anas@ou.edu
1. INTRODUCTION
Several standard geometries like; line, circle, and ellipse, have been widely used in the synthesis of
different antenna array designs in the literature, due to the availability and the ease of implementation of their
parametric equations. These shapes have been implied in hundreds of articles to design antenna arrays, such
as; linear antenna arrays (LAA) [1]–[6], circular antenna arrays (CAA) [7]–[12], and elliptical antenna arrays
(EAA) [13]–[18], and to achieve different objectives like minimizing the maximum sidelobe level or
increasing the directivity of the array. Results’ variation of diverse geometries proves the significant role of
the array’s shape in determining the desired objective in the antenna arrays. Such observation is the main
inspiration to think of creating new optimal geometries that will improve antenna array characteristics.
In 2003, Gielis [19] proposed a new geometrical equation, called the superformula. This geometrical
approach has been used to model and understand numerous abstract, natural, and man-made shapes [19].
Superformula equation can describe different shapes in nature, which can be achieved by modifying the
parameters of the equation that generate various natural polygons [19]. In the literature, the superformula
equation has been implemented, before, in antenna designs [20]–[22].
In this paper, our goal is to minimize the sidelobe level in a new arbitrary antenna array radiation
pattern using three optimization algorithms: antlion optimization (ALO) [23], grasshopper optimization
2. Int J Elec & Comp Eng ISSN: 2088-8708
Synthesis of new antenna arrays with arbitrary geometries based on the superformula (Anas A. Amaireh)
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algorithm (GOA) [24], and a new hybrid optimization algorithm based on ALO and GOA [11]. The rest of
the paper is organized. A brief overview of our new proposed hybrid algorithm is presented in section 2. The
description of the superformula equation and its implementation in the array factor equation are discussed in
section 3. The fitness function and numerical results are detailed in sections 4 and 5, respectively. Finally,
section 6 presents the conclusions of the paper.
2. THE NEW PROPOSED HYBRID ALGORITHM
In study [11], authors proposed a new hybrid algorithm based on two evolutionary algorithms; ALO
algorithm [23] and GOA [24]. The main inspiration behind proposing such hybridization is combining the
characteristics and overcoming the drawbacks of ALO and GOA. The main characteristic of ALO is the
robustness in exploiting the global optima, which has been verified in many articles in the literature
[2], [13], [25], [26]. On the other hand, the social forces in the grasshopper’s swarm show a strong ability to
effectively explore the whole search space in GOA [27]–[29]. Thus, these features give the idea to combine
ALO and GOA in a new hybrid algorithm, which makes a big improvement in their performance.
Moreover, the benefits of hybridization can be shown in overcoming the drawbacks of ALO and
GOA. The usage of the roulette wheel selection method is the main drawback in ALO, since it may cause:
early convergence, loss of diversity, and not enough pressure to select the fittest search agents among the
same fitness search agents. While the disadvantage of GOA exists in the c parameter equation, which
weakens the exploitation process in the algorithm.
Our proposed hybrid algorithm has the ability to efficiently explore and exploit the search space to
reach the global optimum, due to merging the characteristics of both ALO and GOA. The hybrid algorithm
has some factors that enhance the capability of exploration and exploitation processes like the population
nature that reduces local optima stagnation, the repulsion and attraction forces in GOA algorithm, the random
walks and roulette wheel selection method in ALO algorithm, choosing diverse samples of average and less
fitness search agents from next position’s matrix, and the modifications of c parameter in GOA algorithm.
These factors and modifications lead to better diversity of the search agents all over the search space and a
high probability for local optima stagnation avoidance. Moreover, the intensity of search agents in the
proposed algorithm has been decreased rapidly compared with ALO and GOA, due to the modification of the
c parameter and its combination with other shrinking factors. Therefore, the hybrid algorithm guarantees fast
and mature convergence compared with ALO and GOA.
The equation (1) has been used in our proposed algorithms:
𝑋𝑖
𝑑
= 𝑐 (∑ 𝑐
𝑢𝑏𝑑−𝑙𝑏𝑑
2
𝑠(|𝑋𝑗
𝑑
− 𝑋𝑖
𝑑
|)
𝑁
𝑗=1
𝑗≠𝑖
𝑋𝑗 − 𝑋𝑖
𝑑𝑖𝑗
) (1)
where 𝑋𝑖
𝑑
defines the next position for 𝑖𝑡ℎ
grasshopper and 𝑑𝑡ℎ
dimension, 𝑢𝑏𝑑 and 𝑙𝑏𝑑 are the upper and
lower bounds in the dth
dimension, respectively, and 𝑠(𝑟) = 𝑓𝑒
−𝑟
𝑙 − 𝑒−𝑟
where f represents the intensity of
attraction force and l indicates the attractive length scale.
𝑖𝑓 𝑙 ≤ (𝐿)/2
𝑐 = 𝑐𝑚𝑎𝑥 − 𝑙
𝑐𝑚𝑎𝑥−𝑐𝑚𝑖𝑛
(
𝐿
2
)
(2)
𝑖𝑓 𝑙 >
𝐿
2
𝑐 = (𝐿 − 𝑙 + 1) × (
𝑐𝑚𝑖𝑛
𝐿×10𝑙) (3)
Where 𝑐𝑚𝑎𝑥 and 𝑐𝑚𝑖𝑛 are the maximum and minimum values, respectively, L indicates the maximum
number of iterations, and l is the current iteration [24].
𝑐𝑡
=
𝐶𝑡
𝐼
(4)
𝑑𝑡
=
𝑑𝑡
𝐼
(5)
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Where 𝑐𝑡
and 𝑑𝑡
indicate the minimum and maximum of all variables at tth
iteration, respectively, and 𝐼
represents a ratio, such that 𝐼 = 10𝑤 𝑡
𝑇
where t represents the current iteration, T indicates the maximum
number of iterations, and w is a constant that depends on the current iteration [23].
3. SUPERFORMULA AND ARRAY FACTOR EQUATIONS
The two-dimensional superformula representation in polar coordinates is given [19]:
𝜌(𝜑) =
1
{[((|
1
𝑎
cos(𝜑
𝑚1
4
)|)
𝑛2
+(|
1
𝑏
sin(𝜑
𝑚2
4
)|)
𝑛3
)]
1 𝑛1
⁄
}
(6)
where 𝑛1, 𝑛2, 𝑛3, 𝑚1 and 𝑚2 are real numbers. a and b are real numbers, excluding zero, that represent the
major and minor axes, respectively. Variables 𝑚1 and 𝑚2 are responsible for increasing the rotational
symmetry of the shape, while 𝑛2 and 𝑛3 determine whether the shape is inscribed or circumscribed within the
unit circle [19].
The effect of adjusting superformula parameters is shown in Figure 1, which mentions 6 different
shapes generated using the superformula equation with their specific parameters’ values. Figure 1(a)
represents generating an ellipse by tuning the values of 𝑚1, 𝑚2, 𝑛1, 𝑛2, 𝑛3, a, and b into 4, 4, 2, 2, 2, 0.9, and
0.5, respectively, while the equality between a and b, with keeping all other parameters the same, would give
a circle as shown in Figure 1(b). An equisetum stem and square shapes are shown in Figures 1(c) and 1(d)
when the superformula parameters are changed into (7, 6, 6, 10, 0.9, 0.9), and (4, 100, 100, 100, 0.9, 0.9),
respectively. Figure 1(e) represents a starfish with the following superformula parameters values
(5, 1.7, 1.7, 0.1, 0.9, 0.9), and to achieve a rotational shape as Figure 1(f), the parameters must be tuned as
(13/6, 0.3, 0.3, 0.3, 0.5, 0.5).
(a) (b) (c)
(c) (d) (f)
Figure 1. Shapes generated by superformula equation. The numbers inside the brackets refer to (𝑚 (𝑚1=𝑚2),
𝑛1, 𝑛2, 𝑛3, a, b). (a) ellipse (4, 2, 2, 2, 0.9, 0.5), (b) circle (4, 2, 2, 2, 0.6, 0.6), (c) equisetum stem (7, 6, 6, 10,
0.9, 0.9), (d) square (4, 100, 100, 100, 0.9, 0.9), (e) starfish (5, 1.7, 1.7, 0.1, 0.9, 0.9), and (f) rotational shape
(13/6, 0.3, 0.3, 0.3, 0.5, 0.5)
Based on [30], the general array factor for any antenna array can be described as (7):
𝐴𝐹(𝜃, 𝜑) = ∑ 𝐼𝑛𝑒𝑗(𝛼𝑛+𝐾𝜌𝑛.𝑎
̂𝑟)
𝑁
𝑛=1 (7)
Unit
4. Int J Elec & Comp Eng ISSN: 2088-8708
Synthesis of new antenna arrays with arbitrary geometries based on the superformula (Anas A. Amaireh)
6231
where N represents the number of elements, which are assumed to lie on the XY-plane, 𝐼𝑛 and 𝛼𝑛 are the
excitation current and phase for 𝑛𝑡ℎ
element, respectively. The wavenumber 𝐾 =
2𝜋
𝜆
, where 𝜆 is the
wavelength, 𝜌𝑛
represents the position vector of the 𝑛𝑡ℎ
element:
𝜌𝑛
= 𝑥𝑛𝑎
̂𝑥 + 𝑦𝑛𝑎
̂𝑦 (8)
and 𝑎
̂𝑟 is the unit vector for the observation point, which can be written as (9).
𝑎
̂𝑟 = sin 𝜃 cos 𝜑 𝑎
̂𝑥 + sin 𝜃 sin 𝜑 𝑎
̂𝑦 + cos 𝜃 𝑎
̂𝑧 (9)
The x and y coordinates of (6) are presented:
𝑥𝑛 = {[((|
1
𝑎
𝑐𝑜𝑠 (𝜑𝑛
𝑚1
4
)|)
𝑛2
+ (|
1
𝑏
𝑠𝑖𝑛 (𝜑𝑛
𝑚2
4
)|)
𝑛3
)]
−1 𝑛1
⁄
} 𝑐𝑜𝑠 𝜑𝑛
𝑦𝑛 = {[((|
1
𝑎
𝑐𝑜𝑠 (𝜑𝑛
𝑚1
4
)|)
𝑛2
+ (|
1
𝑏
𝑠𝑖𝑛 (𝜑𝑛
𝑚2
4
)|)
𝑛3
)]
−1 𝑛1
⁄
} 𝑠𝑖𝑛 𝜑𝑛 (10)
where 𝜑𝑛 is the angular position of the antenna elements. For uniform array distribution, 𝜑𝑛 is given:
𝜑𝑛 = 2𝜋
(𝑛−1)
𝑁
(11)
using (8)-(10), the array factor expression can be written:
𝐴𝐹(𝜃, 𝜑) = ∑ 𝐼𝑛𝑒𝑥𝑝 {𝑗𝛼𝑛 + 𝑗𝐾 ({[((|
1
𝑎
𝑐𝑜𝑠 (𝜑𝑛
𝑚1
4
)|)
𝑛2
+
𝑁
𝑛=1
(|
1
𝑏
𝑠𝑖𝑛 (𝜑𝑛
𝑚2
4
)|)
𝑛3
)]
−1 𝑛1
⁄
} cos 𝜑𝑛 sin 𝜃 cos 𝜑 + {[((|
1
𝑎
𝑐𝑜𝑠 (𝜑𝑛
𝑚1
4
)|)
𝑛2
+
(|
1
𝑏
𝑠𝑖𝑛 (𝜑𝑛
𝑚2
4
)|)
𝑛3
)]
−1 𝑛1
⁄
} sin 𝜑𝑛 sin 𝜃 sin 𝜑)} (12)
where 𝜃 represents the elevation angle, which is measured from the positive z-axis. 𝜃 is assumed to equal
90°
, since the array factor in the x-y plane will be of interest. It has been assumed, in this paper, that the main
lobe is directed along the positive x-axis, such that; 𝜃𝑜=90°
and 𝜑𝑜 = 0°
. To achieve this, the excitation
phase is assumed to be as (13).
𝛼𝑛 = − 𝐾(𝑥𝑛 sin 𝜃𝑜 cos 𝜑𝑜 + 𝑦𝑛 sin 𝜃𝑜 sin 𝜑𝑜) (13)
4. FITNESS FUNCTION
The main objective of this paper is to minimize the maximum sidelobe level in the array factor of
the proposed antenna arrays. In order to accomplish this, the following fitness function is used [11]:
𝐹𝑖𝑡𝑛𝑒𝑠𝑠 = (𝑊1𝐹1 + 𝑊2𝐹2)/|𝐴𝐹𝑚𝑎𝑥|2
(14)
𝐹1 = |𝐴𝐹(𝜑𝑛𝑢1)|2
+ |𝐴𝐹(𝜑𝑛𝑢2)|2
(15)
𝐹2 = max { |𝐴𝐹(𝜑𝑚𝑠1)|2
, |𝐴𝐹(𝜑𝑚𝑠2)|2} (16)
𝜑𝑛𝑢1 and 𝜑𝑛𝑢2 represent the angles that define the first null beamwidth, FNBW=𝜑𝑛𝑢2 − 𝜑𝑛𝑢1 = 2𝜑𝑛𝑢2.
Furthermore, 𝜑𝑚𝑠1 and 𝜑𝑚𝑠2 are the angles from -180o
to 𝜑𝑛𝑢1, and from 𝜑𝑛𝑢2 to 180o
, respectively, in
which the maximum side lobe level (SLL) is accomplished during the optimization process. The function 𝐹1
minimizes the array factor at 𝜑𝑛𝑢1 and 𝜑𝑛𝑢2 to define the major lobe in the radiation pattern. 𝐹2 minimizes
the radiation pattern in the sidelobe region around the major lobe. 𝐴𝐹𝑚𝑎𝑥 is the maximum value of the array
factor at (𝜃𝑜, 𝜑𝑜). 𝑊1 and 𝑊2 are weighting factors. The purpose of the optimization in this paper is to obtain
new creative geometries for antenna arrays, toward achieving the lowest maximum SLL. Therefore, we are
going to optimize the superformula parameters (𝑛1, 𝑛2, 𝑛3, 𝑚1, 𝑚2, a, b) to get the most suitable geometry
for antenna elements distribution.
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5. NUMERICAL RESULTS
In this section, three different subsections are discussed; 8-element, 12-element, and 20-element.
Three different cases have been optimized using ALO, GOA, and the hybrid algorithm. The first case
discusses creating new geometries by optimizing superformula parameters only (𝑚1, 𝑚2, 𝑛1, 𝑛2, 𝑛3, a, b),
assuming that all elements have unity excitation currents and uniform angular position distribution depending
on (11). So, all the obtained results, in this case, will be compared with uniform EAA results [13]. The
second case discusses the optimization of array elements’ excitation currents along with optimizing the
superformula parameters. In this case, uniform distribution for the antenna elements’ positions has been
assumed depending on (11). The results for this case will be compared with the results of optimizing the
excitation currents in EAA [13]. The third case presents the optimization of the elements’ angular positions
and superformula parameters, assuming unity amplitude currents for all elements. This case’s results are
compared with the corresponding angular positions optimization examples in EAA [13]. It is worth
mentioning, that the EAA has been chosen for the comparison over the CAA due to the generality of the EAA.
All the results in this paper have been optimized through 20 independent runs using 1,000 iterations
and 50 search agents. Three parameters, four parameters, five parameters, and seven parameters of the
superformula equation are optimized for most of the examples. In three parameters optimization, the
parameters: 𝑚, 𝑛2 and 𝑛3 are optimized, assuming that 𝑚1=𝑚2 and 𝑛1=2 (since the ellipse has these
assumptions). While in four parameters optimization; the parameters: 𝑚1, 𝑛1, 𝑛2 and 𝑛3 are optimized, with
𝑚1=𝑚2. In five parameters optimization, all superformula parameters are optimized except a and b. While in
seven parameters optimization, all superformula parameters are optimized. Note that, the major and minor
axes (a and b) have been assumed to equal 0.5 and 0.433 for 8-element antenna array examples, 1.15 and
0.9959 for 12-element examples, and 1.6 and 1.3856 for 20-element examples. Moreover, the range of
optimized parameters is assumed as [1, 50] for 𝑚1, 𝑚2 and 𝑛1, and [-50, 50] for 𝑛2 and 𝑛3. It is worth
mentioning that all distances are in terms of 𝜆.
5.1. Optimizing 8 elements
5.1.1. Optimizing superformula parameters
In this example, an 8-element antenna array with unity amplitudes and uniform angular positions is
optimized to suppress the maximum SLL of the array factor. This can be achieved by optimizing different
parameters in the superformula equation. So, the aim here is to optimize the geometry of the 8-element array, to
get a better maximum SLL compared with standard geometries like EAA. Table 1 shows the optimum values of
superformula parameters and their corresponding maximum SLL, compared with uniform EAA. According to
the obtained results, optimizing 4-parameters, the algorithms ALO, GOA, and the hybrid outperform the
uniform EAA shape by almost 11 dB. In other words, without any change in the excitation currents or the
angular positions, the maximum SLL is improved significantly. Figure 2(a) shows the radiation patterns for
optimizing three and four superformula (S.F.) parameters based on ALO, the hybrid and GOA algorithms
compared with uniform EAA. Figure 2(b) presents the convergence curves for the proposed methods, which
shows that they reach the global optima with a small number of iterations. Box-and-whisker plot is represented
in Figure 2(c), which proves the stability of the hybrid method and ALO over 20 independent runs. Figure 3
represents elements distributions along three-parameter optimized superformula shape based on ALO,
three-parameter optimized based on the hybrid method, four-parameter optimized based on ALO,
four-parameter optimized based on GOA, and four-parameter optimized based on the hybrid method.
5.1.2. Optimizing elements amplitudes and superformula parameters
The optimization of excitation currents and superformula parameters is discussed in this example.
The optimal values of 8 elements currents and three, four, and seven superformula parameters, based on the
hybrid method, compared with 8-element EAA results are tabulated in Table 2. Figure 4(a) illustrates the
array factors depending on the results in Table 2. It can be noticed that generating new creative shapes gives
maximum SLL less than the best results of the optimized EAA by almost 6 dB, which confirms the
significance of generating new geometries of antenna arrays. Figure 4(b) shows the distribution of the
8 elements along the optimized superformula shapes.
Table 1. Optimum values of superformula parameters for 8-element optimization
Method and optimized parameters 𝜑𝑛𝑢2 = 51° [𝑚1, 𝑛2, 𝑚2, 𝑛3,𝑛1, a, b] Max. SLL (dB)
Three-parameter (ALO) [24.0052, 2.82534, 24.0052, 1.59748, 2, 0.5, 0.4330] -15.25
Three-parameter (Hybrid) [24.0099, 1.17679, 24.0099, 3.85461, 2, 0.5, 0.4330] -16.66
Four-parameter (ALO) [36, 44.071, 36, 41.702, 30.3222, 0.5, 0.4330] -17.95
Four-parameter (Hybrid) [12, 18.4426, 12, 17.555, 12.8075, 0.5, 0.4330] -17.94
Four-parameter (GOA) [44.0000, 39.1136, 44.0000, 36.4979, 26.7506, 2, 0.5, 0.4330] -17.66
EAA (Uniform) [4, 2, 4, 2, 2, 0.5, 0.4330] -7.76
6. Int J Elec & Comp Eng ISSN: 2088-8708
Synthesis of new antenna arrays with arbitrary geometries based on the superformula (Anas A. Amaireh)
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(a) (b)
(c)
Figure 2. Results for optimizing superformula parameters for 8-element antenna array (a) radiation patterns
for 8-element optimization, (b) convergence curves for 8-element optimization, and (c) box-and-whisker
plots for 8-element optimization in 20 runs
Figure 3. Distribution of 8-element array over optimized shapes based on ALO, GOA, and the hybrid method
7. ISSN: 2088-8708
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Table 2. Optimum values of excitation currents and superformula parameters for 8-element optimization
N=8
𝜑𝑛𝑢2 = 51°
[𝑚1, 𝑛2, 𝑚2, 𝑛3, 𝑛1, a, b]
[ I1, I2, I3, …, I8]
Max. SLL (dB)
Three-parameter (Hybrid) [7.8031, 2.6905, 7.8031, 1.558, 2, 0.5, 0.4330]
[0.6223, 0.9365, 0.2873, 1.000, 0.6537, 0.9806, 0.2981, 0.9183]
-19.10
Four-parameter (Hybrid) [15.9703, 24.8769, 15.9703, -1.98988, 21.2323, 0.5, 0.4330]
[1.000, 0.7896, 0.7519, 0.8200, 0.0060, 0.8380, 0.7196, 0.8077]
-20.71
Seven-parameter (Hybrid) [15.9746, 15.8241, 20.0043, -5.17621, 20.5931, 0.35375, 0.564119]
[1.000, 0.7004, 0.6741, 0.8938, 0.0903, 0.8358, 0.7965, 0.6413]
-20.93
EAA (ALO) [4, 2, 4, 2, 2, 0.5, 0.4330]
[0.5379, 0.9011, 0.0667, 0.9246, 0.5856, 1.0000, 0.0249, 0.9764]
-14.64
EAA (Hybrid) [4, 2, 4, 2, 2, 0.5, 0.4330]
[0.5666, 1.0000, 0.0516, 0.9700, 0.5609, 0.9586, 0.0690, 0.9887]
-14.60
(a)
(b)
Figure 4. Results for optimizing excitation amplitudes and superformula parameters for 8-element antenna
array (a) radiation patterns for 8-element array and (b) distribution of 8-element array over S.F. optimized
shapes based on the hybrid method
5.2. Optimizing 12 elements
5.2.1. Optimizing angular positions and superformula parameters
S.F. parameters and the angular positions for 12-element antenna array are going to be optimized in
this example. Five and seven parameters of the SF equation are optimized with 12 elements angular positions
based on the hybrid algorithm as mentioned in Table 3. The maximum SLL value obtained by optimizing the
seven-parameter is -16.12 dB, and -15.44 dB for optimizing the five-parameter. It is obvious that using the
SF enhances the maximum SLL by almost 6 dB compared with EAA results. Figure 5(a) shows the radiation
patterns for optimizing five-parameter, seven-parameter, and EAAs based on ALO, GOA, and the hybrid
method. Figure 5(b) represents 12 elements distributions for optimizing five-parameter and seven-parameter SF.
8. Int J Elec & Comp Eng ISSN: 2088-8708
Synthesis of new antenna arrays with arbitrary geometries based on the superformula (Anas A. Amaireh)
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Table 3. Optimum values of angular positions and S.F. parameters for 12-element optimization
N=12
𝜑𝑛𝑢 =22°
[𝑚1, 𝑛2, 𝑚2, 𝑛3,𝑛1, a, b]
[∅1, ∅2, ∅3, …, ∅12] in degrees
Max. SLL (dB)
Five-parameter
(Hybrid)
[4.11638, 23.5199, 4.63125, 24.5776, 24.7026, 1.15, 0.9959]
[1.8360, 29.2719, 73.0149, 132.5429, 152.8216, 175.9628, 195.9229, 254.4374, 281.2805,
316.2005, 325.4463, 345.7353]
-15.44
Seven-
parameter
(Hybrid)
[9.25799, -4.91652, 10.0473, -9.52872, 12.3331, 0.356268, 0.66011]
[7.0744, 86.9813, 100.8709, 115.5467, 136.2431, 169.4018, 202.4140, 209.2568,
227.7020, 234.5620, 248.9834, 254.8533]
-16.12
EAA (ALO) [4, 2, 4, 2, 2, 1.15, 0.9959]
[13.5053, 39.4896, 85.4927, 95.4538, 141.8509, 167.4591, 188.0407, 210.0000, 240.0111,
299.8681, 330.0000, 351.8085]
-9.52
EAA (GOA) [4, 2, 4, 2, 2, 1.15, 0.9959]
[9.2464, 43.3007, 60.0002, 93.9722, 139.9140, 164.3480, 189.0621, 210.0000, 269.7800,
299.1580, 330.0000, 356.2058]
-9.27
EAA (Hybrid) [4, 2, 4, 2, 2, 1.15, 0.9959]
[7.2016, 42.3489, 60.1202, 92.8993, 140.9393, 166.8977, 191.8173, 210.0000, 268.6000,
300.0000, 330.0000, 355.1231]
-9.42
(a) (b)
Figure 5. Results of optimizing angular positions and S.F. parameters for 12-element antenna array
(a) radiation patterns for 12-element arrays and (b) distribution of 12-element
array over S.F. optimized shapes
5.3. Optimizing 20 elements
5.3.1. Optimizing superformula parameters
In this example, the number of array elements is increased to 20. So, the SF parameters are
optimized to generate the optimal geometry of a 20-element array that has minimum suppression for SLL.
Table 4 shows the optimum values for four and seven parameters using our proposed hybrid method, ALO,
and GOA. The best maximum SLL is obtained by optimizing the seven-parameter using the hybrid
algorithm, with max SLL=-16.01 dB, which is 10 dB less than the results of uniform EAA.
Figure 6(a) shows the radiation patterns of the 20-element array for different SF parameters
optimization. Figures 6(b) and 6(c) show convergence curves and box and whisker plots for ALO, GOA, and
the hybrid method, respectively. The uniform distribution of the obtained shapes for optimizing:
four-parameter based on ALO, four-parameter based on the hybrid method, seven-parameter based on ALO,
seven-parameter based on GOA, and seven-parameter based on the hybrid algorithm, are illustrated in
Figure 7. These results, again, prove that optimizing array shapes will significantly improve the max SLL
without changing the excitation currents or angular positions.
Table 4. Optimum values of S.F. parameters for 20-element optimization
𝜑𝑛𝑢2 = 16° [𝑚1, 𝑛2, 𝑚2, 𝑛3,𝑛1, a, b] Max. SLL (dB)
Four-parameter (ALO) [44, 5.63766, 44, 46.3647, 14.9658, 1.6, 1.3856] -14.49
Four-parameter (Hybrid) [36, 5.8958, 36, 49.1193, 15.6256, 1.6, 1.3856] -14.58
Seven-parameter (ALO) [24.0644, 13.1663, 18.3572, 21.9164, 21.0494, 0.931817, 1.76903] -15.78
Seven-parameter (GOA) [21.3997, 8.5576, 2.6021, 11.1608, 24.2988, 1.5287, 1.2400] -12.78
Seven-parameter (Hybrid) [20.0259, 10.76, 1.99925, -8.7371, 14.3647, 1.29043, 0.233034] -16.01
EAA (Uniform) [4, 2, 4, 2, 2, 1.6, 1.3856] -6.88
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(a) (b)
(c)
Figure 6. Results of optimizing S.F. parameters for 20-element antenna array (a) radiation patterns of
optimizing 20-element array, (b) convergence curves for 20-element optimization, and (c) box and whisker
plot for 20-element optimization
Figure 7. Distribution of 20-element array over S.F. optimized shapes based on ALO, GOA,
and the hybrid method
Fitness
function
value
10. Int J Elec & Comp Eng ISSN: 2088-8708
Synthesis of new antenna arrays with arbitrary geometries based on the superformula (Anas A. Amaireh)
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6. CONCLUSION
In this paper, new arbitrary geometries for antenna arrays were proposed. Three evolutionary
algorithms, ALO, GOA, and the hybrid algorithm based on ALO and GOA, were used in the synthesis of the
arbitrary antenna arrays. The objective function was to reduce the maximum sidelobe level with the
constraint of a fixed major lobe beamwidth. Three cases were investigated in this paper; 8-element,
12-element, and 20-element antenna array geometries. Additionally, three different examples were
illustrated; optimizing only the superformula parameters, optimizing superformula parameters and excitation
currents of the antenna array, and optimizing superformula parameters with the angular position of the
antenna array. The results of all cases and examples prove the capability of our geometries to outperform the
standard geometries with a huge difference.
ACKNOWLEDGEMENTS
This research did not receive any specific grant from funding agencies in the public, commercial, or
not-for-profit sectors. The authors declare no conflict of interest in this research work.
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BIOGRAPHIES OF AUTHORS
Anas A. Amaireh is a doctoral student in Electrical and Computer Engineering at
the University of Oklahoma, USA. He obtained his B.Sc. and M.Sc. degrees in Engineering
Technology (major in Wireless Communication Engineering) from Yarmouk University,
Jordan in 2015 and 2018, respectively. His research interests are antenna arrays, numerical
techniques in electromagnetics, metaheuristic algorithms, machine learning, and neural
networks. He can be contacted at email: anas@ou.edu.
Nihad I. Dib obtained his B. Sc. and M.Sc. in Electrical Engineering from Kuwait
University in 1985 and 1987, respectively. He obtained his Ph.D. in EE (major in
Electromagnetics) in 1992 from University of Michigan, Ann Arbor. Then, he worked as an
assistant research scientist in the radiation laboratory at the same school. In Sep. 1995, he
joined the EE department at Jordan University of Science and Technology (JUST) as an
assistant professor and became a full professor in Aug. 2006. His research interests are in
computational electromagnetics, antennas and modeling of planar microwave circuits. He can
be contacted at email: nihad@just.edu.jo.
Asem S. Al-Zoubi received his B.Sc. degree in Electrical Engineering from
Eastern Mediterranean University, Cyprus in 1993, the M.Sc. degree in Electrical Engineering
from Jordan University of Science and Technology, Jordan in 1998, and the Ph.D. degree in
Electrical Engineering/Electromagnetics from the University of Mississippi, USA, in 2008.
Currently, he is an Associate Professor with the Department of Telecommunications
Engineering in Yarmouk University, Jordan. His current research interests include dielectric
resonator antennas, microstrip antennas, and numerical techniques in electromagnetics. Mr.
Al-Zoubi is a member of the IEEE. He can be contacted at email: asem@yu.edu.jo.