Munish Rattan

Design of Linear and Circular Antenna Arrays Using Cuckoo Optimization Algorithm

Cuckoo optimization Algorithm (COA) is employed for the optimization of linear and non- uniform circular antenna arrays. COA is a novel nature inspired computing algorithm which is motivated by the life of Cuckoo. Like other nature-inspired algorithms, COA is also a population-based method and uses a population of solutions to proceed to the global solution. The method of COA is used to determine a set of parameters of antenna elements that provide the required radiation pattern. The efiectiveness of COA for the design of antenna arrays is shown by means of numerical results. Comparison of results of COA is made with that obtained using other popular methods. The results reveal the superior performance of COA as compared to other techniques both for design of linear and circular antenna arrays.

Optimization of Yagi-Uda Antenna Using Simulated Annealing

Simulated annealing (SA) is a stochastic global optimization technique, which has been used successfully for optimization of antenna arrays. In this paper, the use of SA for optimization of gain, impedance and bandwidth of the Yagi-Uda antenna has been presented. To evaluate the performance of designs, a method of moments code NEC2 has been used. Comparative results indicate superiority of using SA over other methods.

A LINEAR ANTENNA ARRAY FAILURE CORRECTION USING FIREFLY ALGORITHM

The element failure of antenna arrays increases the sidelobe power level. In this paper, the problem of antenna array failure has been addressed using Fire∞y Algorithm (FA) by controlling only the amplitude excitation of array elements. A fltness function has been formulated to obtain the error between pre-failed (original) sidelobe pattern and measured sidelobe pattern and this function has been minimized using FA. Numerical example of large number of element failure correction is presented to show the capability of this ∞exible approach.

Optimization of circular antenna arrays of isotropic radiators using simulated annealing

This paper presents the design optimization of circular antenna arrays of isotropic radiators using simulated annealing. The problem has been formulated to achieve a desired value of sidelobe level and a minimum possible value of beamwidth. This is accomplished by jointly optimizing the excitation amplitude and spacing between elements. Simulation examples have been given and comparison has been carried out with particle swarm optimization method.

DESIGN OF A LINEAR ARRAY OF HALF WAVE PARALLEL DIPOLES USING PARTICLE SWARM OPTIMIZATION

Particle Swarm Optimization (PSO) is a new, high performance evolutionary technique, which has recently been used for optimization problems in antennas and electromagnetics. It has been used for optimization of linear array of isotropic radiators, and found to give better results than other companion algorithms. A half wave dipole is a practical radiator, which closely resembles the behavior of an isotropic radiator and is most common array element. In this paper, a linear array of half wavelength parallel dipoles has been optimized for minimum side lobe level and null control using Particle Swarm Optimization. The results obtained show superiority of PSO over conventional method. 132 Rattan, Patterh, and Sohi

Optimization of Cognitive Radio System Using Simulated Annealing

Cognitive radio (CR) technology has introduced a revolution in wireless communication network and it is capable to operate in a continuously varying radio frequency environment that depends on multiple parameters. In this paper, optimization of CR system has been achieved using simulated annealing (SA) Technique. SA is a stochastic global optimization technique that exploits an analogy between the way in which a metal cools and freezes into a minimum energy crystalline structure. SA has been used to meet the quality of service (QoS) that is defined by the user in terms of minimum transmit power, minimum bit error rate, maximum throughput, minimum interference and maximum spectral efficiency. The results obtained by SA are compared with the genetic algorithm (GA) results for the various QoS parameters and it has been observed that SA is outperforming GA in CR system optimization.

A Novel Binary Spider Monkey Optimization Algorithm for Thinning of Concentric Circular Antenna Arrays

ABSTRACT This paper presents a novel binary algorithm named as binary spider monkey optimization (binSMO) for thinning of concentric circular antenna arrays (CCAA). The proposed algorithm has been adapted from a recently developed nature inspired optimization method, spider monkey optimization (SMO). SMO works in continuous domain and as such is not suitable for application to binary optimization problems. The binSMO algorithm has been proposed with inclusion of logical operators in SMO for binary thinning problem. Thinning of an antenna array reduces the maximum side lobe level (SLL) as well as cost and size of antenna array. Thinning of CCAA can be modelled as 0–1 binary integer optimization problem. The proposed binSMO is used to synthesize CCAA in order to reduce the SLL and at the same time keeping the percentage of thinning equal to or more than the desired level. Simulation examples of two ring and ten ring CCAA have been considered. The novel method binSMO gives reduced SLL as compared to the results available in literature of teacher learning based optimization, biogeography based optimization, modified particle swarm optimization, and firefly algorithm. Moreover, the convergence rate of binSMO is faster than the other methods. The results prove the competence and superiority of binSMO to existing metaheuristic algorithms and it has an ability to become an effective tool for solving binary optimization problems.

Optimization of the Return Loss of Differentially Fed Microstrip Patch Antenna Using ANN and Firefly Algorithm

The microstrip patch antenna that have more than two feed points or lines is known as differential fed microstrip patch antenna. In this paper, firefly algorithm (FA) and artificial neural network (ANN) has been applied to a ‘Flower’ shaped differentially fed microstrip patch antenna for optimizing the return loss. This new optimization method is much faster than conventional optimization methods. FA is the new nature-inspired algorithm which is based on the flashing behavior of fireflies in the summer sky in the hot and humid regions. To validate the ability of FA, the results obtained from FA are compared with that obtained using genetic algorithm (GA) and ANN, and it has been observed that FA performs better as compared to GA.

Biogeography-based optimisation of Cognitive Radio system

Biogeography-based optimisation (BBO) is a novel population-based global optimisation algorithm that is stimulated by the science of biogeography. The mathematical models of biogeography describe how a species arises, migrates from one habitat (Island) to another or gets extinct. BBO searches for the global optimum mainly through two steps: migration and mutation. These steps are controlled by immigration and emigration rates of the species in the habitat which are also used to share information between the habitats. In this paper, BBO has been applied to Cognitive Radio (CR) system for optimising its various transmission parameters to meet the quality of service (QoS) that is defined by the user in terms of minimum transmit power, minimum bit error rate (BER), maximum throughput, minimum interference and maximum spectral efficiency. To confirm the capability of biogeography-based optimisation algorithm, the results obtained by BBO are compared with that obtained by using genetic algorithm (GA) for the various QoS parameters, and it has been observed that BBO outperforms GA in system optimisation.

Optimization of Gain, Impedance, and Bandwidth of Yagi-Uda Array Using Particle Swarm Optimization

Particle swarm optimization (PSO) is a new, high-performance evolutionary technique, which has recently been used for optimization problems in antennas and electromagnetics. It is a global optimization technique-like genetic algorithm (GA) but has less computational cost compared to GA. In this paper, PSO has been used to optimize the gain, impedance, and bandwidth of Yagi-Uda array. To evaluate the performance of designs, a method of moments code NEC2 has been used. The results are comparable to those obtained using GA.