Comparison of Evolutionary Algorithms for Synthesis of Non-Uniformly Spaced Linear Array of Unequal Length Parallel Dipole Antennas for Impedance Matching with low side lobe level

Abstract

This work presents a comparative study of three evolutionary algorithms such as quantum particle swarm optimization (QPSO), firefly algorithm (FA) and cuckoo search algorithm (CS) for synthesis of linear array of non-uniformly spaced parallel unequal length very thin dipole antennas for impedance matching of all the antenna elements of an array with low side lobe level. Performance of the above three algorithms for impedance matching are compared here in terms of side lobe level as well as statistical parameters such as global best fitness value, worst fitness value, mean and standard deviation. Mutual coupling effect exists between the parallel dipole antennas and it is analyzed by induced electro-motive force (EMF) method, assuming Current distribution on each dipole to be sinusoidal. In addition to it, the obtained results from simulation of the entire optimization algorithm on Matlab is also validated by results obtained from FEKO analysis. One example is presented to show the effectiveness of the proposed approach. Moreover the applied method seems very effective for a linear array of dipole antennas; however, the principle can easily be extended to other type of arrays