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Single and multi-objective design of Yagi-Uda antennas using computational intelligence
Design of Yagi-Uda antennas is a challenging problem since antenna characteristics such as gain, input impedance, maximum sidelobe level etc., are known to be extremely sensitive to the design variables viz., element lengths and their spacings. Although, population-based, stochastic, zero-order methods like genetic algorithm (GA) and evolutionary algorithm (EA) are attractive choices for such classes of problems, their successful application requires a number of additional inputs (e.g. scaling and aggregating factors to deal with constraints and objectives) that is not easy for a designer to provide. We introduce a population-based, stochastic, zero-order optimization algorithm and use it to solve single and multiobjective Yagi Uda design optimization problems. The algorithm is attractive as it is computationally efficient and does not require additional user inputs to model constraints or objectives. One single objective and two multiobjective Yagi Uda design examples are presented. The first example highlights the limitations of using an aggregate objective function in design optimization, while the second and the third examples illustrate the performance of our optimization algorithm for multiobjective problems.