Genetic algorithm optimization and its application to antenna design

Synthesis of antenna patterns employing iterative optimization techniques has been studied by many authors. However, successful application of these approaches to pattern synthesis has usually been limited to relatively simple arrays or has required careful, intelligent selection of the optimization starting points dictated by the nature of the optimization techniques used and the functions being optimized. This is because conventional functional optimization techniques are either based on greedy, local optimization methods such as gradient methods or consist of random walk solution space searches. In either case, these conventional techniques are often poorly suited to the task of arbitrary pattern synthesis in 1D and 2D antenna arrays due to the high dimensional, multimodal functional domains involved. In addition, traditional optimization techniques usually require the object function to be, at the very least, continuous and, in many cases to be differentiable, placing severe limitations on the form and content of the object function. This paper presents a radically different and relatively new functional optimization methodology known as genetic algorithm (GA) optimization that overcomes the above-mentioned problems of the traditional techniques and discusses how GA optimization is applied to 1D and 2D antenna design.<
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