In the last decade nature-inspired Optimizers such as genetic algorithms (GAs), particle swarm (PSO), ant colony (ACO), honey bees (HB), bacteria feeding (BFO), firefly (FF), bat algorithm (BTO), invasive weed (IWO) and others algorithms, has been successfully adopted as a powerful optimization tools in several areas of applied engineering, and in particular for the solution of complex electromagnetic problems. This chapter is aimed at presenting an overview of nature inspired optimization algorithms (NIOs) as applied to the solution of complex electromagnetic problems starting from the well-known genetic algorithms (GAs) up to recent collaborative algorithms based on smart swarms and inspired by swarm of insects, birds or flock of fishes. The focus of this chapter is on the use of different kind of natured inspired optimization algorithms for the solution of complex problems, in particular typical microwave design problems, in particular the design and microstrip antenna structures, the calibration of microwave systems and other interesting practical applications. Starting from a detailed classification and analysis of the most used natured inspired optimizers (NIOs) this chapter describes the not only the structures of each NIO but also the stochastic operators and the philosophy responsible for the correct evolution of the optimization process. Theoretical discussions concerned convergence issues, parameters sensitivity analysis and computational burden estimation are reported as well. Successively a brief review on how different research groups have applied or customized different NIOs approaches for the solution of complex practical electromagnetic problem ranging from industrial up to biomedical applications. It is worth noticed that the development of CAD tools based on NIOs could provide the engineers and designers with powerful tools that can be the solution to reduce the time to market of specific devices, (such as modern mobile phones, tablets and other portable devices) and keep the commercial predominance: since they do not require expert engineers and they can strongly reduce the computational time typical of the standard trial errors methodologies. Such useful automatic design tools based on NIOs have been the object of research since some decades and the importance of this subject is widely recognized. In order to apply a natured inspired algorithm, the problem is usually recast as a global optimization problem. Formulated in such a way, the problem can be efficiently handled by natured inspired optimizer by defining a suitable cost function (single or multi-objective) that represent the distance between the requirements and the obtained trial solution. The device under development can be analyzed with classical numerical methodologies such as FEM, FDTD, and MoM. As a common feature, these environments usually integrate an optimizer and a commercial numerical simulator. The chapter ends with open problems and discussion on future applications.
