## 1 Introduction
MOAs explore larger portions of design space than gradient-based optimizers (GBOs), thus increasing the probability of finding a global optimum (i.e., the actual properties to be identified). For this reason, MOAs have progressively replaced GBOs, such as sequential unconstrained minimization techniques, with penalty functions (SUMT), sequential quadratic programming (SQP), trust region algorithms (TRA), etc. However, according to the no-free-lunch theorem, no metaheuristic algorithm inherently outperforms all other metaheuristic optimizers in all optimization problems [25; 26]. Another issue in metaheuristic optimization is the computational cost of the search process. Hence, metaheuristic algorithms for material characterization should neither be computationally expensive nor converge to \"false\" values of material/structural properties. Furthermore, convergence behavior should be independent of the setting of the algorithm's internal parameters. The blooming of metaheuristic methods favored by their inherent ability of finding global optima (or at least nearly global optimum solutions) as well as by the exponentially increasing computational power has led many researchers to use these algorithms in inverse problems. A comprehensive literature survey on applications of metaheuristic algorithms to mechanical characterization of materials is given in [27]. The interpretation of structural engineering from the perspective of inverse problems and the role played by metaheuristic algorithms and artificial intelligence in such a context are thoroughly reviewed in [28]. It should be noted that the mechanical identification process becomes even more complicated in the case of soft matter, which is usually characterized by nonlinear constitutive behavior and large deformations. Further complications derive from the presence of material anisotropy and structural inhomogeneity. The selected material model (usually stated by one of the many hyperelastic laws available in the literature) should fully \"exploit\" the features of the chosen experimental protocol for characterization. Furthermore, the inverse FE procedure should focus on material models as well as on the choice of deformation range. Although the latter aspect may sound obvious, it is often not properly taken into account by analysts (see, for example, the discussion reported in [29]). Optical methods (Oms) [30; 31; 32; 33] are highly suited for mechanical characterization of materials because they provide accurate full-field information on displacements, strains and stresses (i.e., the basic entities that form the error functional \(\Omega\) defined in inverse mechanics problems) in real time. Oms may utilize visible light as well as X-rays and infrared radiation. Any target measurement scale can be achieved by properly selecting the illumination type and the experimental setup parameters. Nanoscale resolution can be achieved by adopting non-conventional illumination and super-resolution techniques. In Oms, light wave fronts are modulated by the deformed body's surface [30; 31; 32; 33]. This results in the formation of a system of fringes (isodisplacement loci) whose spatial frequency relates with the strain field. Displacement information may be extracted from a deterministic signal (e.g., moire follows the spatial modulation of a regularly spaced grating), a random signal (e.g., speckle exploits the modulation of surface roughness caused by deformation) or image correlation (e.g., a random pattern printed on the specimen's surface is modulated by the applied load). The multiscale capability of Oms isvery useful in the characterization process if the measured displacement components are very different in magnitude. For example, the 3D displacement field of a soft membrane may be determined by combining intrinsic moire and projection moire, respectively, to measure in-plane and out-of-plane displacements [34]. However, displacement components measured optically should be expressed in the same reference system set in the numerical simulation of the experiments.