## 2 State of the Art
### Metaheuristic Algorithms
The referenced work [35] interlinks the parameter-free Constraint Handling Technique (CHT) with a Finite Element (FE) matrix model coded in MATLAB to calculate the frequency. The findings reveal that the WSAR finds lighter designs and satisfies all the frequency limits better than other MAs. Most MAs work probabilistically and use standard input parameters such as the population size and generation number. Apart from these generic control parameters, there are more algorithm-specific ones that need to be fine-tuned, including the mutation probability, crossover probability, inertia weight, and the number of onlookers, scout, or employed bees, as well as the harmony memory consideration rate, pitch adjusting rate, etc. It is even more important to set these parameters appropriately since they are more sensitive and directly impact the algorithm performance. Improper tuning can increase the degree of complexity or result in local optimal solutions. Another optimization technique developed from swarm intelligence is termed the Jaya algorithm, which optimizes for the best solution, while, at the same time, distancing itself from the worst [36]. The Jaya algorithm provides a parameter less solution to avoid these problems. It has been implemented on 24 constrained and 30 unconstrained benchmark functions and compared with other efficient optimization techniques like GA, PSO, ABC, and TLBO, yielding superior results by reaching the global optimum in fewer function evaluations [37]. Moreover, hybrid algorithms have been advanced to take advantage of some algorithms, while, at the same time, minimizing the negatives. The Particle Swarm Optimizer Cultural (PSOC) hybrid method as a PSO algorithm integrated with the Cultural Algorithm (CA) to increase the PSO convergence speed and decrease the computational time expenses. The study [38] presents the success of the proposed hybrid algorithm by solving truss structure problems and comparing the performances with the traditional PSO and other MAs. The Hybrid Intelligent GA (HIGA) combines the GA with a Deep Neural Network (DNN) to improve the algorithm's truss optimization process. Unlike using heuristic optimization alone or at the same level as GA, this method achieves more satisfactory results through successive integration with Machine Learning (ML). The research proves that the HIGA approach enhances the accuracy and the speed of the optimization procedures while noting that its deployment may demand the involvement of specialists in ML field [39]. Even though those algorithms are powerful, problems arise in real-life situations, mostly in the form of Multi-Objective Optimization (MOO) problems in which several objectives represent two or more conflicting factors. These problems are usually solved using Non-dominated Sorting Genetic Algorithm II (NSGA-II), NSGA-III, and Decomposition-based approaches. NSGA-II was later formed by [PERSON] et al. in 2002 as an enhancement of NSGA to solve the problems, including the technicality of the procedure and the shortcomings in the diversity of solutions. In the NSGA-II algorithm, all analyzed solutions are divided into non-dominated categories and their quality is determined using the fast non-dominated sorting technique, while the crowded-comparison operator and the preservation of the diversity of solutions are used to identify a rich set of Pareto-optimal solutions [40]. NSGA-III, also known as reference-point-based NSGA-II, is an enhanced version of NSGA-II created by [PERSON] and [PERSON] in 2014. NSGA-III uses a reference-point-based approach to enhance the capability of preserving diversity when the objective space dimension increases. The algorithm preprocesses the objective space and places each solution into a comparable framework based on the reference point. Through a niche-preserving operation, solutions that are most aligned with the reference points are selected [41]. Another method involves decomposing the problems called the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D).