## 1 Introduction and Literature Review
[23] proposed a multi-stage damage detection of truss structures, in which the MKEn is firstly implemented to discover possible damage locations, and then a model updating approach using hybrid flexibility and mode shapes objective function with the symbiotic organisms search for accurate damage identification. In their other work, [PERSON] et al. [24] implemented MKEn-based sensitivity for the successful damage identification in composite beams. [PERSON] et al. [25; 26] also used the sensitivity of MKEn for damage identification in 2D simple supported beams. [PERSON] et al. [27] developed a new damage identification index based on the relative differences of MSEn and MKEn, particularly for beam-like structures. Additionally, [PERSON] et al. [28] developed a two-stage damage identification approach in which the MKEn is first used for initial assessment of structural damage, whereas the MSEn is later used to formulate an objective function that was solved by PSO algorithm. Results showed successful damage identification in truss structures. [PERSON] et al. [29] proposed a cross modal sensitivity of MSEn and MKEn for damage identification in beam and offshore platform structures. Another research work was conducted by [PERSON] and [PERSON] [30], in which the sensitivity of MKEn was utilized for damage identification in simple supported beams. From the aforementioned literature review, it can be concluded that the MKEn is a useful damage indicator that can be used for the inverse problem of SHM. Nevertheless, it has rarely been used in research. Moreover, it has not been explicitly used as the main objective function within the inverse problem of structural damage identification, and has only been used as a damage indicator for localizing possible damage in structures. Therefore, it is very useful to use the MKEn and integrate it with MSEn to formulate an efficient objective function that can extract maximum damage information from structures. In order to solve the inverse analysis of structural damage identification, soft computing techniques have been widely used [31]. In particular, the recent advances in evolutionary computation have boosted soft computing techniques into more practical applications, due to their robust foundation and high computational abilities. Evolutionary computation involves various algorithms that mimic several nature processes, such as evolutionary theory [32], physics-based phenomena [33], herding and breeding behaviors of creatures [34; 35], swarm-based behavior [36], etc. Such algorithms are highly recommended for solving inverse problems; i.e., the problem of inverse analysis of structural damage. Several representative research studies have been reported. For example, [PERSON] and [PERSON] [37] tested the efficiency of slime mold optimization for structural damage identification in various case studies. In other studies, conducted by the same authors, the gray wolf optimizer (GWO) was successfully utilized for similar case studies [38], as well as the multi-verse optimizer (MVO) [39]. [PERSON] and [PERSON] [40] developed a two-stage damage identification method using the sunflower optimization algorithm for application to plate structures. Moreover, [PERSON] et al. [41; 42] studied the efficiency of the Lichtenberg optimization algorithm for structural damage identification in plate structures. [PERSON] et al. [43] compared several algorithms for the purposes of damage identification in truss structures. In other studies, conducted by the same research group, several evolutionary algorithms were used, such as the hybrid evolutionary-neural network approach [44], the modified PSO algorithm in [45], and the improved velocity strategy PSO in [46], etc. [PERSON] et al. [47] developed the plasma generation algorithm for damage detection in truss structures. Similarly, other research studies have been conducted by [PERSON] et al. [48; 49] for the same purpose. Other new research studies were observed in [50; 51; 52].