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During the last decade, the unprecedented increase in the affordable computational power has strongly supported the development of optimization techniques for designing antennas. Among these techniques, genetic algorithm  and particle swarm optimization  could be mentioned. Most of these techniques use physical dimensions of an antenna as the optimization variables, and require solving Maxwell's equations (numerically) at each optimization step. They are usually slow, unable to handle a large number of variables, and incapable of finding the globally optimum solutions. In this paper, we are proposing an antenna optimization technique that is orders of magnitude faster than the conventional schemes, can handle thousands of variables, and finds the globally optimum solutions for a broad range of antenna optimization problems. In the proposed scheme, termination impedances embedded on an antenna structure are used as the optimization variables. This is particularly useful in designing on-chip smart antennas, where thousands of transistors and variable passive elements can be employed to reconfigure an antenna. By varying these parasitic impedances, an antenna can vary its gain, band-width, pattern, and efficiency. The goal of this paper is to provide a systematic, numerically efficient approach for finding globally optimum solutions in designing smart antennas.