Biswa Binayak Mangaraj

Improved Adaptive Bacteria Foraging Algorithm in Optimization of Antenna Array for Faster Convergence

This paper proposes an improved adaptive approach involving Bacterial Foraging Algorithm (BFA) to optimize both the amplitude and phase of the weights of a linear array of antennas for maximum array factor at any desired direction and nulls in specific directions. The Bacteria Foraging Algorithm is made adaptive using principle of adaptive delta modulation. To show the improvement in making the algorithm adaptive, results for both adaptive and nonadaptive algorithms are given. It is found that Adaptive Bacteria Foraging Algorithm (ABFA) is capable of improving the speed of convergence as well as the precision in the desired result.

DESIGN, ANALYSIS AND OPTIMIZATION OF V-DIPOLE AND ITS THREE-ELEMENT YAGI-UDA ARRAY

This paper presents an optimum design technique ofan asymmetric V-dipole antenna and its a three-element Yagi-Uda array using Genetic Algorithm (GA). The optimization parameter for the V-dipole is the directivity and that for the Yagi-Uda array are the input impedance and directivity. The theoretical analysis has been done using a Moment-Method technique in a very simple step-by-step way, and subsequently the GA is applied for obtaining the optimized parameters. Comparative results are provided for 3-elements straight dipole Yagi and V-dipole Yagi array. Further, analysis for directivity with respect to included angle is given for the GA based optimization problem that gives an important aspect in the design ofV-Yagi.

Implementing Taguchi and cuckoo search to optimize LAA

This paper presents a global optimization technique named as Taguchis method (TM) and an evolutionary natured-inspired metaheuristic cuckoo search (CS) optimization method and applied to linear antenna array (LAA) designed for minimum side lobe levels and nulls at the desired directions. TM was developed on the basis of the orthogonal array (OA) concept, which offers systematic and efficient characteristics which effectively reduces the number of tests required in an optimization process. Another nature-inspired evolutionary CS optimization algorithm is implemented which is capable of solving general N-dimensional, linear and nonlinear optimization problems. The Lévy flight concept of CS optimization method shows its random walk which helps to generate new solutions randomly. The array geometry synthesis is first formulated as an optimization problem with the goal of side lobe level (SLL) suppression and null prescribed placement in certain directions, and then solved by both TM and CS method. Obtained results show that the TM has the tremendous ability to converge quickly.

Optimization of Rectangular Patch Antenna at 5GHz Using Bat Search Algorithm

A rectangular micro strip antenna is widely used now a days. It is analyzed with the length, width of the rectangular patch and height, dielectric constant of substrate being optimized to make the antenna resonate at 5GHz. The optimization is done through programs based on bat algorithm using MATLAB R2009b and the obtained results are used to design of the antenna using CST Studio Suite 2011. The S-parameter plot is then observed after the transient solver step of the antenna is completed.

Performance comparison of PSO optimized mutually coupled linear array antenna with Yagi-Uda antenna

In this paper, two different structures (six elements and fifteen elements) of linear array antenna are designed considering mutual coupling among all the elements. Popular Particle Swarm Optimization (PSO) method is used to optimize the Directivity (D) and Half Power Beam Width (HPBW) by varying the input design parameters, length of the elements and spacing between them. Simulation is performed by MATLAB (7.8.0) and verified by MININEC (14.0). Then the obtained Directivity and HPBW of respective linear array antennas are compared with Yagi-Uda antennas having same number of elements, optimized with different evolutional optimization techniques, from the previously published results, to evaluate the performance of the linear array antenna.

Designing and optimizing a mutually coupled Planar Array Antenna using PSO for UHF application

Particle Swarm Optimization (PSO) algorithm is introduced here to optimize the element spacings and lengths of a mutually coupled Planar Array Antenna to have maximum Directivity (D), minimum Half Power Beam Width (HPBW) and Front to Side Lobe Level (FSLL). The array is simulated with MOM structure code using MATLAB (7.8.0.347) and verified using standard MININEC (14.0) software package for UHF application. The results obtained using PSO for our structure are compared with different structures of antennas of other authors, optimized using various optimization techniques for its performance evaluation.

Bacteria Foraging Algorithm in Antenna Design

A simple design procedure to realize an optimum antenna using bacteria foraging algorithm BFA is proposed in this paper. The first antenna considered is imaginary. This antenna is optimized using the BFA along with a suitable fitness function formulated by considering some performance parameters and their best values. To justify the optimum design approach, one 12-element Yagi-Uda antenna is considered for an experiment. The optimized result of this antenna obtained using the optimization algorithm is compared with nonoptimized conventional result of the same antenna to appreciate the importance of optimization.

Linear Dipole Antenna Array design and optimization using Gravitational Search Algorithm

This paper presents an application of nature inspired population based algorithm namely Gravitational Search Algorithm (GSA) to Linear Dipole Antenna Array (LDAA) optimization. The design parameters of the LDAA to be optimized are the length of the dipole antenna elements and spacings out of each of the group of two neighbor elements. The self developed GSA using MATLAB is used to optimize the design parameters of the LDAA to determine a set of performance parameters of LDAA such as directivity and side lobe level. The major goal of antenna design is to achieve narrow beamwidth (high directive gain) and low side lobe level. The optimized radiation patterns indicate that the application of the GSA to our optimization problem is found to be a promising one for obtaining a higher directivity and lower side lobe level.

Optimum Design and Performance Analysis of Dipole Planar Array Antenna with Mutual Coupling Using Cuckoo Search Algorithm

In this paper, a recently developed meta-heuristic optimization algorithm namely Cuckoo Search (CS) is introduced for analyzing a 4 × 4 dipole planar array (DPA) taking mutual coupling into account. The CS algorithm is employed to optimize the DPA using the multi-objective fitness function considering the Directivity (D), Half Power Beam Width (HPBW), and Front to Side Lobe Level (FSLL) of the antenna. The array is simulated using self developed Method of Moments (MOM) codes using MATLAB (7.8.0.347) and verified using standard MININEC (14.0) software package. Finally, the optimized results are compared with the results of other antenna arrays obtained using various other popular optimization techniques to evaluate the performance of the DPA. The computation time using CS is much less than PSO and 4 × 4 DPA provides higher D with low FSLL.

A new approach of performance comparison between microstrip rectangular and circular patch antenna using PSO

Presently optimization of patch antennas are performed using available softwares like Cs microwave studio, HFSS, IE3D. in this communication a new approach is considered, where source code of the structure and optimization method, both in MATLAB are used and linked to each other. The paper aims for assessment of choosing a suitable patch between circular and rectangular patch for a particular frequency of operation which can be extended for a frequency range. It considers both design parameters like area, height of substrate, dielectric constant of substrate and performance parameters such as directivity, half power beam width in E-plane (EHPBW) and H-plane (HHPBW), reflection co-efficient, VSWR and return loss. The patches are optimized using Particle Swarm Optimization (PSO) and their results are compared in order to decide the best patch.