Design of Uniform Linear Antenna Array Using Enhanced Moth Flame Optimization Algorithm

Abstract

In this work, a new optimization algorithm named as enhanced moth flame optimization (E-MFO), has been designed by modifying the basic moth flame optimization (MFO). This proposed algorithm has been employed for the synthesis of uniform linear antenna arrays (LAA). In E-MFO, four modifications have been proposed to overcome the drawbacks of MFO. A random attraction model is added to enhance the exploration capability, influence of best flame has been incorporated to improve the exploitation. Moreover, an adaptive step size and division of iterations is applied to maintain a good balance between the exploration and exploitation. To validate the applicability of E-MFO, it has been applied to twenty benchmark functions and results are compared with other meta-heuristic algorithms like BA, DE, FA, FPA and BFP. The effect of population and dimension size on the performance of E-MFO has been discussed. Also, statistical testing of E-MFO has been done to prove its significance. The numerical results show the superior performance of E-MFO over other algorithms in terms of convergence rate and solution quality. LAA is a difficult and non-linear electromagnetic problem. Hence, to verify the performance of E-MFO, it is used to synthesise the complex LAA design problem. The basic goal of the antenna design is to achieve minimum side lobes and null steering by optimizing the amplitude excitations. The performance of E-MFO has been evaluated by using seven different examples of LAA and results are compared with other well-known algorithms. The results show that E-MFO outperforms other algorithms in terms of reduction of side lobes and null control.