This chapter presents several antenna array design cases by using different evolution-ary algorithms (EAs) and comparing results. The synthesis of antenna arrays plays a very important role in communication systems. Array synthesis is a classic and challenging optimization problem, which has been extensively studied using several analytical or stochastic methods. The increased use of such arrays creates more challenges upon the antenna engineers. More requirements, such as pattern shaping, low profile, wideband/narrowband, and interference cancellation; and more limitations such as power dissipation and antenna size, lead to the urgent need for simple, time saving, and efficient optimization tech-niques. Common optimization goals in array synthesis are the sidelobe level (SLL) suppression and the matching of the mainlobe to a desired pattern. Thus, the opti-mization problem is usually to find a set of element excitations and/or positions that closely match a desired pattern. The desired pattern shape can vary widely depending on the application. Several new synthesis and optimization techniques have emerged in the last two decades that mimic biological evolution, brain function, or the way biological entities communicate in nature. Several of these methods have been applied to the array design problem
Antenna array design using EAs, Page 1 of 2
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