Optimizations of antennas using heuristic algorithms supported by the multilevel fast multipole algorithm

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2015

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Önol, Can

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In this study, an optimization environment based on heuristic algorithms supported by the multilevel fast multipole algorithm (MLFMA) is presented for different antenna problems involving either excitation or geometry optimizations. The heuristic algorithms are implemented in-house by aiming more effective interactions between electromagnetic solvers and optimization algorithms, instead of black box interactions. Excitation optimizations of various array geometries for desired radiation characteristics are investigated in numerical experiments involving extremely large optimization spaces. Implemented heuristic algorithms are improved via alternative mechanisms and compared to available toolbox of MATLAB. In addition to excitation optimizations, we consider more challenging optimizations involving geometric modifications. In this context, two different types of pixel antennas are studied and optimized. Furthermore, the designs obtained via optimizations are fabricated in low cost setups based on commercial inkjet printers. Measurements on fabricated samples demonstrate the effectiveness of the optimizations, as well as the efficacy of the low-cost production mechanism that fully benefits from the advantages of inkjet printing. Finally, approximate forms of MLFMA are examined for dynamic accuracy control during the optimizations. Effects of using these approximate forms and possible strategies for employing them in order to increase the speed of the optimizations are discussed.

Subject Keywords

Antennas (Electronics)., Antenna arrays., Genetic algorithms.

URI

http://etd.lib.metu.edu.tr/upload/12619141/index.pdf
https://hdl.handle.net/11511/24914

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

C. Önol, “Optimizations of antennas using heuristic algorithms supported by the multilevel fast multipole algorithm,” M.S. - Master of Science, Middle East Technical University, 2015.