## 1 Introduction and Literature Review
Among the novel evolutionary computation methods, the gradient-based optimizer (GBO) is a newly introduced MH for solving numerical optimization problems [53]. It mainly involves the stochastic conversion of gradient theory into a population-based optimization algorithm. The main mathematical proposal of GBO is to modify the Newtonian gradient method using an evolutionary paradigm with the innovative local-escaping mechanism for overcoming complex optimization problems. Therefore, it is important to explore the performance of GBO to solve the inverse analysis of structural damage problems in addition to boosting its search mechanism to make it more suitable to overcome complex computational tasks associated with the inverse analysis of assessment of structural damage. Hence, the GBO is improved by using two search mechanisms. First, oppositional-based learning (OL), which is a powerful optimization assistant tool that evaluates the oppositional of a considered solution and compares the two solutions in order to choose a better one. This efficient tool can help to accelerate the convergence of the algorithm and explore more search areas, which suits problems with multimodality and multiple local optima, such as the problem of structural damage assessment. In addition, the local search mechanism of a powerful PSO variant, the unified PSO (UPSO), can also be incorporated with the main computational framework of GBO to boost the local search ability of GBO and provide accelerated performance to deal with the high computational problem of the inverse analysis of structural damage. Therefore, another major contribution of this article is to design, test, and verify a new optimization algorithm called the oppositional unified particle swarm gradient-based optimizer (OL-UPSGBO), which is specifically designed to overcome the complex optimization problem of the inverse analysis of structural damage. The main contributions of this study can be summarized as: (i) a new hybrid objective function that mainly depends on modal kinetic and strain energies for the problem of inverse analysis of structural damage is introduced; (ii) a new optimization algorithm called the OL-UPSGBO, which incorporates the OL, the UPSO, and the GBO, is developed; (iii) the new algorithm is first tested and verified using the most challenging benchmark function set, which is the Congress of Evolutionary Algorithms (CEC-2017) and it is compared and verified against some recently developed evolutionary algorithms; (iv) the framework of structural damage assessment is introduced and case studies are presented using incomplete and noisy modal responses. The rest of this study is divided into the following sections: (i) Section 2 presents the theoretical background of the problem of inverse analysis of structural damage, as well as the development of the new hybrid objective function; (ii) the novel OL-UPSGBO is introduced in Section 3 and verification experiments are presented; (iii) the total structural damage identification framework and case studies, as well as the results of the case studies including the overall challenges, achievements, and recommendations are discussed in Section 4; (iv) the concluding remarks and future scope are presented in Section 5.