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A Soft Computing Hybridization Technique for Antenna Optimization

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Thanks to the exploding growth of radio mobile and wireless communication applications, microstrip antenna with its advantages of low cost and flexible fabrications, emerges as the most suitable candidate for Wi-Fi integrated devices. In this sector, direct antenna synthesis by full-wave analysis is computationally expensive in terms of time and memory usage. Considering the complexity of antenna optimization, in order to achieve feasible, robust and low-cost solutions, the recent trends tend to involve evolutionary algorithms and bio-inspired computation. In this article, the hybrid technique between Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) is implemented to optimize a proximity coupled feed antenna made of dual-annular rings.
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Computational Intelligence; Microstrip Antenna; Artificial Neural Network

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