Review of Soft Computing Methods for Microwave and Millimeter-Wave Design

Abstract

In this chapter, we present a state-of-the-art review on the present use of soft computing methods for the design applications in microwave and millimeter-wave domain [44]. Since long time, the literature on soft computing was confined to the methods such as genetic algorithms, artificial neural network, fuzzy logic, and their variations and hybridizations. During last decade, few other swarm intelligence based algorithms such as particle swarm optimization, ant colony optimization, and bacterial foraging optimization have emerged. Microwave researchers also observe these techniques and try to adopt them for various microwave design applications. In this chapter, a review of microwave design using five soft computing methods namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Neural Network (ANN), and Support Vector Machine (SVM) has been presented. Out of these methods, ANN and GA have been widely exploited by microwave researchers. Though efforts have been made to review related works of all five methods used for microwave design applications, emphasis is given on recent methods, namely, PSO, SVM and BFO. For BFO and SVM, no much has been reported in literature for microwave design.