Linear Antenna Array Pattern Synthesis Using Elephant Swarm Water Search Algorithm
08 Mar 2019-International Journal of Information Engineering and Electronic Business (MECS Publisher)-Vol. 11, Iss: 2, pp 10-20
TL;DR: The results show that ESWSA is very efficient process for achieving desired radiation pattern while amplitude only control performed better compare to the others two controlling process for all benchmark problems.
Abstract: Linear antenna array pattern synthesis using computational method is an important task for the electronics engineers and researchers. Suitable optimization techniques are required for solving this kind of problem. In this work, Elephant Swarm Water Search Algorithm (ESWSA) has been used for efficient and accurate designing of linear antenna arrays that generate desired far field radiation pattern by optimizing amplitude, phase and distance of the antenna elements. ESWSA is inspired by water resource search procedure of elephants during drought. Two different fitness functions for two different benchmark problem of linear antenna array have been tested for validation of the proposed methodology. During optimization, three types of synthesis have been used namely: amplitude only, phase only and position only control for all cases antenna array. The results show that ESWSA is very efficient process for achieving desired radiation pattern while amplitude only control performed better compare to the others two controlling process for all benchmark problems.
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TL;DR: In this paper, a sparrow search algorithm (SSA) and its modification are applied to the electromagnetics and antenna community for the first time in order to solve the antenna array optimization problem, and three types of benchmark test functions are calculated to verify the effectiveness of the modified algorithm.
Abstract: Antenna arrays play an increasingly important role in modern wireless communication systems. However, how to effectively suppress and optimize the side lobe level (SLL) of antenna arrays is critical for communication performance and communication capabilities. To solve the antenna array optimization problem, a new intelligent optimization algorithm called sparrow search algorithm (SSA) and its modification are applied to the electromagnetics and antenna community for the first time in this paper. Firstly, aimed at the shortcomings of SSA, such as being easy to fall into local optimum and limited convergence speed, a novel modified algorithm combining a homogeneous chaotic system, adaptive inertia weight, and improved boundary constraint is proposed. Secondly, three types of benchmark test functions are calculated to verify the effectiveness of the modified algorithm. Then, the element positions and excitation amplitudes of three different design examples of the linear antenna array (LAA) are optimized. The numerical results indicate that, compared with the other six algorithms, the modified algorithm has more advantages in terms of convergence accuracy, convergence speed, and stability, whether it is calculating the benchmark test functions or reducing the maximum SLL of the LAA. Finally, the electromagnetic (EM) simulation results obtained by FEKO also show that it can achieve a satisfactory beam pattern performance in practical arrays.
14 citations
TL;DR: Results show that the improved CS with reverse learning and invasive weed operators (ICSRLIWO) has higher solution accuracy and faster convergence speed than other comparison algorithms, and has advantages in solving the array antenna beam pattern optimization problems.
6 citations
References
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Book•
01 Jan 1982
TL;DR: The most up-to-date resource available on antenna theory and design as mentioned in this paper provides an extended coverage of ABET design procedures and equations making meeting ABET requirements easy and preparing readers for authentic situations in industry.
Abstract: The most-up-to-date resource available on antenna theory and design Expanded coverage of design procedures and equations makes meeting ABET design requirements easy and prepares readers for authentic situations in industry New coverage of microstrip antennas exposes readers to information vital to a wide variety of practical applicationsComputer programs at end of each chapter and the accompanying disk assist in problem solving, design projects and data plotting-- Includes updated material on moment methods, radar cross section, mutual impedances, aperture and horn antennas, and antenna measurements-- Outstanding 3-dimensional illustrations help readers visualize the entire antenna radiation pattern
14,065 citations
"Linear Antenna Array Pattern Synthe..." refers methods in this paper
...Examples of analytical techniques include the well-known Taylor method and the Chebyshev method [5]....
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TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
Abstract: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to information-theoretic aspects of optimization and benchmark measures of performance are also presented. Other issues addressed include time-varying optimization problems and a priori "head-to-head" minimax distinctions between optimization algorithms, distinctions that result despite the NFL theorems' enforcing of a type of uniformity over all algorithms.
10,771 citations
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TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
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TL;DR: This paper demonstrates the application of genetic algorithms (GAs) in array pattern synthesis by presenting three examples: two for linear arrays and one involving linear and planar arrays.
Abstract: This paper demonstrates the application of genetic algorithms (GAs) in array pattern synthesis. GAs have the ability to escape from local minima and maxima and are ideally suited for problems where the number of variables is very high. We present three examples: two for linear arrays and one involving linear and planar arrays.
381 citations
"Linear Antenna Array Pattern Synthe..." refers methods in this paper
...Different metaheuristic or their modification algorithms have been used like genetic algorithms [12], simulated annealing [13], differential evolution algorithm [14], bacterial foraging algorithm [15], plant growth simulation algorithm [16], Taguchi’s and self-adaptive differential evolution [17], biogeography based optimization [18], bees algorithm [19], particle swarm optimization [20], [31], cuckoo Search [21], Seeker optimization algorithm [22], invasive weed optimization [23], harmony search algorithm [24], firefly algorithm [25], evolutionary search algorithm [26], differential search algorithm [27], cat swarm optimization [28], hybrid cuckoo search [29], backtracking search optimization algorithm [30] etc for linear antenna array...
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TL;DR: A numerical technique for pattern synthesis in arrays is presented, which allows one to find a set of array coefficients that steer the main beam in a given direction and yield sidelobes meeting a specified criterion, if such a setof array coefficients exists.
Abstract: A numerical technique for pattern synthesis in arrays is presented. For a given set of elements, the technique allows one to find a set of array coefficients that steer the main beam in a given direction and yield sidelobes meeting a specified criterion, if such a set of array coefficients exists. If the pattern specifications cannot be met with the given elements, the algorithm finds the best attainable pattern. The advantage of this technique is that it can be used with an arbitrary set of array elements. Different elements in the array can have different element patterns, and the array can have arbitrary nonuniform spacing between elements. The synthesis technique is based on adaptive array theory. The given array elements are assumed to be used as the elements of an adaptive array. The main beam is pointed in the proper direction by choosing the steering vector for that direction, and the sidelobes are controlled by introducing a large number of interfering signals at many angles throughout the sidelobe region. The algorithm iterates on the interference powers until a suitable pattern is obtained. >
164 citations