## 2 Literature Review
### Related Studies
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author(s)** & **Types of PSO** & **Aims** & **Key Findings** \\ \hline \multirow{4}{*}{[29]} & Particle swarm optimization and firefly algorithm (FFA) & This paper aimed to compare the performance of PSO and the firefly algorithm by using almost ten non-linear functions. The time and mean values of the non-linear functions were used as the input and output variables. & The result showed that the non-linear functions and time value is smaller compared with the firefly algorithm. \\  & Particle swarm optimization PSO & The aim was to apply PSO on four test functions to achieve an adequate selection of particles. Particle swarm optimization-recombination and dynamic linkage discovery (PSO-RDL) & The aim was to use this hybrid PSO-RDL to solve economic dispatch in the power system. & The study implied that not all test functions were improved in terms of performance. They discovered that the performance of PSO-RDL was similar to a modified particle swarm optimization (MPSO) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Types of particle swarm optimization (PSO). parameters, and their structure is quite different from an older generation of PSOs. Another significant feature about them is that they only use information that is current and not past information. A considerable number of researchers have used different types of PSO in solving real-life problems. Researchers such as [40] used different kinds of PSO variants on dixian-szego testing. [PERSON] et al. [41] came up with an evolutionary game particle swarm optimization (EGPSO) to model particle behaviours and patterns by applying replicator dynamics and multi-start methods. [PERSON] et al. [42] used an adaptive PSO that is dependent on spider behaviour. [PERSON] et al. [43] explained the implications of removing obsolete particles from a current PSO iteration. [PERSON] et al. [44] used the combination of gradient and PSO to eliminate immature convergence. For the validation and predictions of different types of data, there are various pre-existing models such as artificial neural networks (ANNs) [45; 46], finite element method (FEM), and finite strip method (FSM) [47; 48]. The finite element method is a common technique created by ABAQUS and ANSYS. They have been applied to different types of experimental investigations to either validate or predict the structural properties of certain specimens [49]. Even though the finite element has been widely applied over the years for prediction, it still has disadvantages such as significant time executions, a different variation of outputs, and a high number of data requisitions [50]. In recent years, artificial intelligence (AI) methods have performed significant functions advancing various engineering aims and objectives [51]. Artificial neural networks are a sub-branch of Artificial intelligence techniques. They can solve three distinct types of issues such as (1) approximation of functions, (2) classification, and (3) prediction of time series [52]. However, considering these issues, an ANN model is commonly trained and developed by using optimization approaches. Conventional algorithms such as but not limited to backpropagation algorithms have been used so many times in training artificial neural networks [52]. However, the major setback of using conventional algorithms is that they are usually stuck in local extremums and exhibiting difficulties in crossing plateaus of the error function associated with the landscape. The error landscape is the deficiencies of the conventional algorithms. To address these deficiencies, metaheuristic optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) [53], and imperialist competitive algorithm [54] can be applied to trained artificial neural networks to address these deficiencies. The global search, a significant characteristic of these algorithms, can improve the ANN model performance in some situations.