## 1 Introduction
Commonly-used objective functions using SM include both natural frequencies and MAC-based objective functions [10]. Further, FE model updating and damage detection have also been tried with SM using objective functions based on strain energy residuals [11] and modal flexibility [12]. To alleviate the issues associated with SM being attracted to local minima instead of global minima in difficult optimization problems, global optimization algorithms (GOAs) have received the attention of researchers [13]. The main difference between the two approaches is that SM works on a single point, whereas GOAs work in parallel on many points consecutively [14; 15]. Evolutionary algorithms (EAs) are part of GOAs, which deal with mechanisms inspired by biological evolution such as cross over, mutation and selection. In the context of model updating and damage detection, several studies have been conducted using different objective functions with GOAs such as genetic algorithm (GA) [15; 16; 17; 18], simulated annealing (SA) [19] and particle swarm optimization (PSO) [20]. GA was applied to a simulated beam, as well as a frame structure using frequencies and MAC-based objective functions to efficiently detect damage [16; 17]. Multiobjective GA was also used to detect damage in a simulated beam and signature bridge structure based on MAC and flexibility-based objective functions [21]. However, due to the simplification of a 3D model to a 2D model, the first three vertical modes were matched, and torsional modes were not included in model updating. Furthermore, multiobjective multicriteria GA was studied for damage detection and estimation using MAC and flexibility-related objective functions [18]. A numerical beam and wing plate experimental structure was also investigated using both GA and SA with the frequency response function (FRF)-based objective function [19], and a new bended SA algorithm was also proposed for improvement of the results. Damage detection in thin plates based on modal data using a PSO-GA-based approach has been presented by [PERSON] and [PERSON] [22]. In a study, breeder GA was also used for an adaptive FE model for dome structures [23]. Another study [24] proposed hybrid GA and SA to improve the solution and convergence. A multiobjective formulation has also been developed for FE model updating using GA [25]. PSO was also used for assessment of beam and truss structures using the frequency-based modal residual [20,26]. Modeling errors were also investigated using GA and PSO in a multiobjective sense for damage estimation using frequency and mode shape residuals [27]. Ill-conditioning is a topic of concern in the FE model updating procedure. In the case of an ill-conditioned system and measurement noise, the system parameters in different sets can produce the same set of responses at distinct locations of the search space; hence, the solution cannot be affirmed as unique. Three remedies are introduced to overcome the ill-conditioning of the problem, i.e. : (i) modification of the space of the system parameters; (ii) modification of measured data; and (iii) modification of the error function [28]. The first two remedies are about the relationship between the number of parameters and measured data. The first remedy calls for a reduction in the number of uncertain parameters for model updating. The most commonly-used approach is grouping similar elements into one parameter [29]. Efforts to increase the number of independent vibration responses have been made in pursuit of the second remedy [30] by using laser vibrometry or by testing the structure in different configurations [31]. Diverse inverse problems have been attempted by adopting the third remedy, called a regularization technique [28,32]. Regularization techniques make use of the prior information to get an additional check for the minimization problem by modifying the error function. The unknown parameters are assumed to be closer to the known nominal values.