Algorithms Of Pattern Synthesis Arrays

With the development of high-speed computer technology, intelligent optimization algorithms are widely used in the area of antenna design, because of its robust, fit for muti-object and without requiring for differentiability. There are two intelligent optimization algorithms are discussed in this thesis-- Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO), and the applications of these algorithms in synthesis antenna.Genetic Algorithm(GA) is a random search method based on the biological evolution. The best solution is obtained though selecting, replicating, crossing and mutating. The theory and characteristic of GA is presented. The element's current amplitude and phase of linear array antenna are optimized to reduce the maximum side level and control the beam shape, with an improved real-code Genetic Algorithm. This improved Genetic Algorithm is also used to design pattern reconfigurable antenna, simulation results about a pattern reconfigurable array antenna which has two switchable states are given to indicate that the method discussed in this paper is efficiency to design pattern reconfigurable antenna.Particle Swarm Optimization Algorithm (PSO) is a new intelligent optimization algorithm, compared with GA, PSO has simpler principle, fewer parameters, faster convergence, and more stable robust. Several improved PSO are discussed to reform the shortage of traditional PSO. An improved PSO with mutation is used to reduce the maximum side level, control pattern synthesis of antenna, form deep zeros and notches in demanded positions.