Swarm intelligence denotes a class of new stochastic algorithms inspired by the collective social behavior of natural entities (e.g., birds, ants, etc.). Such approaches have been proven to be quite effective in several applicative fields, ranging from intelligent routing to image processing. In the last years, they have also been successfully applied in electromagnetics, especially for antenna synthesis, component design, and microwave imaging. In this paper, the application of swarm optimization methods to microwave imaging is discussed, and some recent imaging approaches based on such methods are critically reviewed. 1. Introduction Microwave imaging denotes a class of noninvasive techniques for the retrieval of information about unknown conducting/dielectric objects starting from samples of the electromagnetic field they scatter when illuminated by one or more external microwave sources [1]. Such techniques have been acquiring an ever growing interest thanks to their ability of directly retrieving the distributions of the dielectric properties of targets in a safe way (i.e., with nonionizing radiation) and with quite inexpensive apparatuses. In recent years, several works concerned those systems. In particular, their ability to provide excellent diagnostic capabilities has been assessed in several areas, including civil and industrial engineering [2], nondestructive testing and evaluation (NDT&E) [3], geophysical prospecting [4], and biomedical engineering [5]. The development of effective reconstruction procedures is, however, still a quite difficult task. The main difficulties are related to the underlying mathematical problem. In fact, the information about the target are contained in a complex way inside the scattered electric field. In particular, the governing equations turn out to be highly nonlinear and strongly ill posed. Consequently, inversion procedures are usually quite complex and time consuming, especially when high resolution images are needed. In the literature several approaches have been proposed for solving this problem. In particular, two main classes of algorithms can be identified. Deterministic [6–21] and stochastic strategies [22–33]. Deterministic methods are usually fast and, when converge, they produce high quality reconstructions. However, their main drawback is that they are local approaches, that is, they usually require to be started with an initial guess “near” enough to the correct solution. Otherwise, such approaches can be trapped in local minima corresponding to false solutions. Moreover, in most cases, it is
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