Performance Evaluation of Stochastic Algorithms for Linear Antenna Arrays Synthesis Under Constrained Conditions

Particle Swarm Optimization (PSO) is a high performance optimization technique recently introduced to solve antenna array synthesis problems to handle multiple degrees of freedom. An important problem facing the user of PSO is its parameters selection, as well as an efficient scheme to improve the optimization process. This paper proposes the use of a global asynchronous PSO update scheme in the synthesis of linear antenna arrays to solve the complex design restrictions imposed by a constrained mask. It is shown that PSO is more efficient than the well-known method of Genetic Algorithms (GA) even under constrained conditions, in terms of simplicity and computational burden.