Particle Swarm Optimization Versus Genetic algorithm for an adaptive uniform circular array

This paper focuses on comparison of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) that are applied to obtain beam forming of an adaptive Uniform Circular Array (UCA). UCA geometry is targeted because of its symmetry in configuration which enables the adaptive array to scan azimuthally with minimum changes in its beam width and side lobe levels. PSO and GA are used to calculate the complex weights of the antenna elements in order to adapt the antenna to the changing environment. Comparisons are made in the context of performance of PSO and GA algorithms.PSO is less complex and has a very fast convergence over GA. The Particle Swarm Optimizer shares the ability of GA to handle arbitrary cost functions but with much simple implementation it clearly demonstrates better possibilities for its wide use in electromagnetic optimization.