## 2 State of the Art
### Metaheuristic Algorithms  
The Genetic Algorithm (GA), a well-established method that mimics evolutionary theory, is one of the most widely used AI methods among researchers [20; 21; 22; 23; 24; 25]. GAs are a subset of Evolutionary Algorithms (EAs) that utilize techniques based on natural biological processes, including mutation, crossover, and elimination, to find optimal solutions to complex problems such as the combinatorial knapsack problem and the travelling salesman problem. The main principle here is [PERSON]'s survival of the fittest idea to iteratively remove unfit solutions [26]. Differential Evolution (DE) is also one of the more advanced EAs and is regarded as an improvement over GAs. DE utilizes crossover and mutation like GAs but works on vectors in a multi-dimensional search space. The variants of DE are denoted as DE/x/y/z, where x specifies the method in which a vector is chosen for mutation, y represents the different perturbations between pairs of vectors, and z indicates the crossover method. It is reported that DE provided superior performance compared with other methods in terms of the number of required functions [27; 28]. On the other hand, Ant Colony Optimization (ACO) emulates the foraging behaviour of ants, where ants leave pheromone trails on the path to indicate advantageous ways for other colony members to follow. With time, the intensity of those pheromone's paths accumulates and converges towards one optimal way to reach the objective, guiding the decision-making process [29; 30]. Similarly, Particle Swarm Optimization (PSO) mimics the collective behaviour of bird or fish flocks, addressing problems by utilizing a population-based approach. The particles move through the search space, where they adjust their position and velocity based on both their own experience and the experience of neighbouring particles, collectively guiding the swarm toward optimal solutions through iterative processes [31]. More recent and successful MA approaches, such as improving the Follow the Leader (iFTL) Algorithm, improve upon the Follow the Leader (FTL) technique that copies the movement of a sheep in a flock. The study [32] focuses on redefining the FTL algorithm to define both the exploration and exploitation phases to solve the issue that arises from using the initial approach. The improved algorithm showed better outcomes than other algorithms and provided efficiency in the exploitation occurrence along with exploration. It was verified with several benchmarks such as truss designs; the outcomes reveal enhanced reliability and precision. The Bonobo Optimizer (BO) algorithm is another recent optimization technique based on the primates' social and mating strategies, such as fission-fusion. In the context of truss structure sizing, the algorithm with discrete and continuous variables has been coded in MATLAB in the referenced article and has proven effective regarding the number of individual runs and convergence rate. However, the effectiveness of this algorithm should be examined in future work to apply it to more general and complicated structures in comparison with the truss systems [12]. The Artificial Bee Colony (ABC) method, inspired by the honeybees' food-searching activity, is utilized in [33] to find optimum structures and member sizes of the truss to minimize buckling, stress, and displacement limit constraints. Its practical impact pertains to showcasing the algorithm's efficiency, which may be a clear way to enhance more research by utilizing the described approach in different applications and increase the chances of obtaining lighter designs. However, the base parameters of the ABC algorithm that have the most impact on its success were not set up properly in the studies [33; 34]. The Weighted Superposition Attraction-Repulsion (WSAR) Algorithm is one of the recent swarm-intelligence-based MAs to solve the multiple-frequency-constraint's complex problem of truss optimization.