Design Of Circular Antenna Arrays Research Articles

Novel enhanced Salp Swarm Algorithms using opposition-based learning schemes for global optimization problems

Salp Swarm Algorithm (SSA) is a recent approach with a simple implementation, few parameters, and low computational cost. SSA has been used in different optimization problems and provided competitive results compared to other popular metaheuristics. However, like other metaheuristics, SSA also faces problems such as lack of population diversity and premature convergence in local optima. Its other drawback is that it uses a random population initialization procedure that may produce solutions in a less effective starting point (i.e., initial population) with a poor diversity. In SSA, a single spatial search mode is used to update the position of followers, which makes it easy to fall in the local optima. SSA is more inclined to the exploitation phase, so it cannot always conduct a global search well and, in some cases, cannot find the global optimal solution. This work proposes an in-depth investigation of applying Opposition-based Learning (OBL) and its impact on the search space’s coverage, accuracy, exploration and exploitation, and convergence of the SSA’s search process. Hence, five enhanced hybrid SSA-OBL algorithms were developed and compared to the traditional SSA, Chimp Optimization Algorithm (ChOA), Giza Pyramids Construction (GPC), and Arithmetic Optimization Algorithm (AOA) in 15 IEEE CEC-2015 benchmark global minimization functions with 30, 50, and 100 dimensions. The Wilcoxon Signed Rank Test is used to test the statistical significance of the results. The proposed opposition-based SSAs statistically outperform basic SSA, ChOA, GPC, and AOA, respectively, with an average of 7.6, 15, 13.4, 15 out of 15 functions with 30 dimensions, 8, 15, 15, 15 with 50 dimensions, and 9.2, 15, 15, and 15 with 100 dimensions. Besides, we also applied the proposal in solving real-world optimization problems such as circular antenna array design problem and spacecraft trajectory optimization problem, and the opposition-based SSAs statistically outperform other competitive algorithms. The experiment results showed that incorporating different OBL schemes in SSA enhanced its exploration ability leading to better performance.

Electric Charged Particles Optimization and its application to the optimal design of a circular antenna array

In this paper, a new metaheuristic, Electric Charged Particles Optimization (ECPO) algorithm, is developed. This algorithm is inspired by the interaction (forces exerted) between electric charged particles. It this algorithm not all the particles interact with each other, only selected ones. Then the way they interact with each other is defined by the selected strategy among the three available strategies. Therefore, there are several combinations possible between the number of interacting particles and strategies to find the most suitable one for the problem in hand which will help the algorithm to solve a wide range of optimization problems. The performance of the developed algorithm is first tested on the set of problems used for single objective real parameter algorithm competition that was held in the congress on evolutionary computation 2014. Then, the ECPO has been applied to optimal design of circular antenna array for sidelobe level reduction. The obtained results are then compared with other well-known metaheuristics using statistical tools. The analysis of the experimental results shows that the ECPO is a very competitive optimization algorithm.

Reusing the Past Difference Vectors in Differential Evolution-A Simple But Significant Improvement.

Differential evolution (DE) has established itself as a simple but efficient population-based, nonconvex optimization algorithm for continuous search spaces. Unlike the conventional real-coded genetic algorithms (GAs) and evolution strategies (ESs), DE uses a mandatory self-referential mutation for its population members, each of which are perturbed with the scaled difference(s) of the individuals from the current generation (iteration). These difference vectors determine the direction of the search moves for the individuals. However, unlike the better individuals, they are not retained in the elitist evolution cycle of DE. In this paper, we show that by archiving the most promising difference vectors from past generations and then by reusing them for generating offspring in the subsequent generations, we can strikingly improve the performance of DE. This strategy can be integrated with any classical or advanced DE variant with no serious overhead in time or space complexity. We demonstrate that when combined with the DE-based winners of the IEEE Congress on Evolutionary Computation (CEC) 2013, 2014, and 2017 competitions on real parameter optimization, the simple reuse strategy leads to a statistically significant performance improvement in the majority of test cases. We further showcase the efficacy of our proposal on a practical optimization problem concerning the design of circular antenna arrays with a prespecified radiation pattern.

Beam pattern design of circular antenna array via efficient biogeography-based optimization

In this paper, an efficient biogeography-based optimization (EBBO) method is proposed to synthesize the circular antenna arrays (CAA) for specific radiation beam pattern properties and null controls. The proposed method achieves the desired beam patterns by jointly optimizing the excitation currents as well as the spaces between the array elements. Three improved components, including chaotic search theory, model learning method and a new random perturbation operator, are introduced into the standard biogeography-based optimization (BBO) to improve the performance of the algorithm. Simulation results show that the maximum sidelobe level obtained by EBBO can be suppressed effectively compared with other algorithms. Moreover, the null controlling performance of EBBO is the best among the algorithms.

A novel artificial bee colony algorithm for radar polyphase code and antenna array designs

When designing radar, mobile, or satellite communication systems, optimization problems often come out and need to be handled to accomplish certain requirements. Polyphase code design and circular antenna array design problems are considered in this paper. To deal with the two problems, a novel artificial bee colony (ABC) algorithm is proposed. A population reduction method is used in the proposed algorithm. Large population is initially set for exploration search, while population is reduced to a small one for exploitation search. Moreover, a new boundary repair method is proposed to amend the candidate solutions that violate boundary constraints. It hybridizes four popularly used repair methods in literature. The resulting algorithm is called population reduction and hybrid repair ABC (PRHRABC). Experiments are conducted on the two design problems. Results show that PRHRABC presents promising performance in dealing with the problems compared with standard and a state-of-the-art ABC algorithms.

Beam-steering in a three-element circular antenna-array

A simple and volume efficient circular antenna-array design with a low profile programmable beam rotation mechanism was presented. The proper selections of the rotation vector and the excitation coefficients of rectangular array-elements were made for rotation of the beam. The proposed rotation mechanism was capable to rotate the radiation pattern at any desired speed and to transmit in any desired direction, and the design included the ease of construction. Although simulating the radiation pattern using FEKO EM simulator, two basic functions, the power splitter and the introduction of phase difference, were included in feed network of microstrip circuit to divide the power and then individually feeding the each patch after introducing the desired phase difference.

Circular antenna array pattern analysis using radial basis function neural network

A method is proposed to design circular antenna array for the given gain and beam width using Artificial Neural Networks. In optimizing circular arrays, the parameters to be controlled are excitation of the elements, their separation, lengths and the circle radius. This paper deals about finding the parameters of radiation pattern of given uniform circular antenna array. Initially, the network is trained with a set of input-output data pairs. The trained network is used for testing. The training data set is generated from MATLAB simulation with number of elements N=5, 10, 15 and 20 elements of uniform circular array, respectively, distributed over a given circle, assuming 20 training cases. The number of input nodes, hidden nodes and output nodes are 20, 20 and 1, respectively. Predicted values of the neural network are compared with those of MATLAB simulation results and are found to be in agreement. This work establishes the application of Radial Basis Function Neural Network (RBFNN) for circular array pattern optimization. RBFNN is able to predict the output values with 97% of accuracy. This work proves that RBFNN can be used for circular antenna array design.

Circular antenna array synthesis using firefly algorithm

In this article, the design of circular antenna arrays (CAAs) and concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level (SLL) reduction is studied. The newly proposed global evolutionary optimization method; namely, the firefly algorithm (FA) is used to determine an optimum set of weights and positions for CAAs, and an optimum set of weights for CCAAs, that provides a radiation pattern with optimum SLL reduction with the constraint of a fixed major lobe beamwidth. The FA represents a new algorithm for optimization problems in electromagnetics. It is shown that the FA results provide a SLL reduction that is better than that obtained using well-known algorithms, like the particle swarm optimization, genetic algorithm (GA), and evolutionary programming. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:139–146, 2014.

CIRCULAR ANTENNA ARRAY DESIGN BY USING EVOLUTIONARY SEARCH ALGORITHMS

Evolutionary Search Algorithms (EA) have been inten- sively used in solving numerical optimization problems. Since design of antenna arrays is a numerical optimization problem, EAs have been intensively used in solving antenna arrays design problems. Although EAs are widely used in antenna array design problems, a performance comparison study of the intensively used EAs for circular antenna ar- ray design problem has been scarcely studied. In this paper, 3 difierent circular antenna array design problems have been solved by using 15 difierent evolutionary search algorithms (i.e., ABC, ACS, BSA, CK, CLPSO, CMAES, DE, E2-DSA, EPSDE, GSA, JADE, JDE, PSO, SADE, S-DSA). The objective function designed for solution of the relevant circular antenna array design problems ensures minimization of side lobe levels, acquisition of maximum directivity, and null control of the non-uniform, planar circular antenna array. Obtained statistical analysis results show that S-DSA solves the relevant circular antenna array design problems statistically better than the other evolutionary algorithms used in this paper.

A Mesh Adaptive Basin Hopping Method for the Design of Circular Antenna Arrays

The design of circular antenna arrays is a challenging optimization problem, which requires ad-hoc methods to fulfill the engineering requirements. In this work, we introduce the Mesh Adaptive Basin Hopping algorithm to tackle such problem effectively; the experimental results show that the new approach proposed outperforms the state-of-the-art methods, both in terms of quality of the solutions and computational efficiency.

Non-uniform Concentric Circular Antenna Array Design using IPSO Technique for Side Lobe Reduction

In this paper the optimal design for maximum sidelobe level (SLL) reduction of three-ring Concentric Circular Antenna Array (CCAA) is determined using a novel Particle Swarm Optimization (PSO) technique namely Improved Particle Swarm Optimization (IPSO) algorithm. Standard PSO is also adopted for the comparative optimization. The present text assumes non-uniformly excited and non-uniform inter-element spacing array with a design goal of maximizing SLL reduction using the above two evolutionary optimization techniques. It is shown that among all the designs, the three-ring structure containing (N1=4, N2=6, N3=8) elements proves to be the optimal design owing to the highest SLL reduction achieved by each technique. IPSO yields grand minimum SLL (-31.86dB) for the above optimal set.

SYNTHESIS OF THINNED PLANAR CONCENTRIC CIRCULAR ANTENNA ARRAYS --- A DIFFERENTIAL EVOLUTIONARY APPROACH

Circular antenna array design is one of the most important electromagnetic optimization problems of current interest. The problem of designing a large multiple concentric planar thinned circular ring arrays of uniformly excited isotropic antennas is considered in this paper. This antenna must generate a pencil beam pattern in the vertical plane along with minimized side lobe level (SLL). In this paper, we present an optimization method based on an improved variant of one of the most powerful real parameter optimizers of current interest, called Difierential Evolution (DE). Two sets of difierent cases have been studied here. First set deals with thinned array design with the goal to achieve number of switched ofi elements equal to 220 or more. The other set contains design of array while maintaining side lobe level (SLL) below a flxed value. Both set contains two types of design, one with uniform inter-element spacing flxed at 0.5‚ and the other with optimum uniform inter-element spacing. The half-power beam width of the synthesized pattern is attempted to maintain flxed at the value equal to that of a fully populated array with uniform spacing of 0.5‚. Simulation results of the designed thinned arrays are compared with a fully populated array for all the cases to illustrate the efiectiveness of our proposed method.

Book reviews - The theory and design of circular antenna arrays

Book reviews - The theory and design of circular antenna arrays

Analysis and design of circular antenna arrays by matrix methods

The antenna arrays considered in this paper consist of identical parallel dipoles equally spaced around and perpendicular to a circle. Application of the method of moments to the analysis problem yields a matrix description of the array. Economies of computation are achieved by exploiting the array symmetries. The matrix description of the array leads to a network description for feed design and a relationship between principal H -plane pattern synthesis and pattern synthesis for an array of point sources. Some classes of pattern synthesis problems are considered and calculated examples presented.