World Applied Sciences Journal 26 (2): 232-238, 2013 ISSN 1818-4952 © IDOSI Publications, 2013 DOI: 10.5829/idosi.wasj.2013.26.02.1387 Corresponding Author: S.U. Khan, School of Engineering and Applied Sciences, ISRA University, Islamabad, Pakistan. 232 Application of Firefly Algorithm to Fault Finding in Linear Arrays Antenna S.U. Khan, I.M. Qureshi, F. Zaman, A. Basit and W. Khan 1 2,4 3 3 3 School of Engineering and Applied Sciences, ISRA University, Islamabad, Pakistan 1 Department of Electrical, Air University, Islamabad, Pakistan 2 Department of Electronic Engineering, IIU, H-10, Islamabad, Pakistan 3 Institute of Signals, Systems and Soft Computing (ISSS), Islamabad, Pakistan 4 Submitted: Aug 25, 2013; Accepted: Oct 27, 2013; Published: Nov 12, 2013 Abstract: Detection of faulty elements in an array of sensors is a practical issue which has applications in radar, satellite and mobile communication. Due to element failure, the radiation pattern disturb in terms of sidelobe level, damage of nulls and increase of bandwidth. In this paper, we develop a new technique based on Firefly Algorithm (FA) to locate the position of faulty elements in a linear array. The FA is a global optimization method and come under the umbrella of swarm optimization. The cost function is used as a fitness evaluation function which defines an error between the degraded far field power pattern and the estimated one. The proposed algorithm is used successfully for the detection of complete, as well as, for partial faulty elements position. Various simulation results are evaluated for 34 elements Chebyshev array of specific Side Lobe Level (SLL), to validate and test the performance of the proposed algorithm. Key words: Array Antenna Firefly Algorithm Fault Finding INTRODUCTION genetic algorithm to find the defective element's position Fault finding in antenna array is a hot problem and rapid technique for finding the defective elements in which has direct application in radar, satellite and mobile antenna arrays. In [11] artificial neural network is used for communication. The antenna array of such applications finding the three faulty elements in a small array of have large number of radiating elements and the 16-elements. Bucci et al. [12] studies the uncertainty of possibility of getting failure of one or more elements the solution in the continuous and the discrete on-off increases due to unforeseen reasons. The malfunction of cases using amplitude-only pattern and then propose an one or more elements increases the Side Lobe Level (SLL), adapted genetic algorithm to solve the problem in the displacement of nulls from their unique positions and discrete case. Nan Xu et al. [13] used machine learning increases the bandwidth of the power pattern. Array optimization for the detection failure of antenna array antenna has the advantage that the weights of the active elements. elements can be re-adjusted to achieve the required In this paper, we develop a new technique based on radiation pattern. R. J. Mailloux [1] used digitally Firefly Algorithm (FA) to locate the position of faulty beamformed array for the correction of array element elements in a linear array. The FA is a global optimization failure. In the literature different techniques are developed method and come under the umbrella of swarm for this compensation that numerically finding a set of optimization. The cost function is used as a fitness weights of the active elements that minimized the fitness evaluation function which defines an error between the function [2-7]. But before using these compensation degraded far field power pattern and the estimated one. methods one has to locate the faulty elements. Authors in The minimum of cost function will give us the location of their previous work [8] have used the symmetrical element faulty element. The proposed algorithm is used failure technique to achieve the required null depth level successfully for the detection of complete, as well as, for and first null beamwidth. In [9] J. A. Rodríguez et al. used partial faulty elements position. Simulation results are in planar array while in [10] the same author used a simple