Linear antenna array synthesis using Novel Particle Swarm Optimization

In this paper the synthesis of linear array geometry with minimum sidelobe level using a new class of Particle Swarm Optimization technique namely Novel Particle Swarm Optimization (NPSO) is described. The NPSO algorithm is a newly proposed, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and simulated annealing, the NPSO algorithm is much easier to understand and implement and requires the least of mathematical pre-processing. The array geometry synthesis is first formulated as an optimization problem with the goal of sidelobe level (SLL) suppression, and then solved by the NPSO algorithm for the optimum element locations and current excitations. Five design examples are presented that illustrate the use of the NPSO algorithm, and the optimization goal in each example is easily achieved. Standard Particle Swarm Optimization (PSO) is adopted to validate the results of the NPSO algorithm.