# An Overview of Variants and Advancements of PSO Algorithm
## 1 Introduction
According to [PERSON] and [PERSON] [11], these concepts of exploration and exploitation abilities were not fully understandable because, on one hand, there was no accepted opinion among the researchers about exploration and exploitation, and on the other hand, an increase in one's capability resulted in the weakness of the other one. Therefore, a proper balance is imperative between the exploration and exploitation capabilities for the success of any algorithm to solve the optimization problem. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of Algorithm** & **Year** & **Description** & **Ref. No.** \\ \hline Artificial hummingbird algorithm & 2022 & [PERSON] et al. proposed an artificial hummingbird algorithm (AHA) to tackle optimization problems and proved its effectiveness over other metaheuristics with experimental results. & [1] \\ \hline Chimp Optimization Algorithm ([PERSON] and [PERSON] (2020a)) & 2022 & [PERSON] et al. presented an enhanced chimp optimization algorithm (ECROA) and analyzed its performance on 12 classical benchmark functions and 15 & [2] \\  & CEC2017 benchmark functions. & & \\ \hline Rat swarm optimization ([PERSON] et al. (2021)) & 2021 & [PERSON] et al. presents swarm-based rat swarm optimization and tested its performance on unimodal, multimodal and CEC-15 special session & [3] \\  & & benchmark functions. & \\ \hline African Culture’s Optimization Algorithm ([PERSON] et al. (2021)) & 2021 & A new metaheuristics, namely African Culture’s Optimization Algorithm (AVOA) is proposed by [PERSON] et al. They proved it as a best algorithm on 30 of 36 benchmark functions. & [4] \\ \hline Dragonfly optimization & 2021 & [PERSON] and [PERSON] proposed dragonfly node identification algorithm (DNAIA) and evaluated its robustness and efficiency using statistical analysis, & [5] \\  & & convergence rate analysis, Wilcoxon test, freferman rank test, and analysis of variance on classical as well as modern IEEE CEC 2014 benchmark functions. & [6] \\ \hline Horse herd optimization algorithm & 2021 & In order to solve high dimensional optimization techniques, [PERSON] et al. developed a new metaheurisite algorithm called the Horse herd Optimization Algorithm (HOA).Through statistical results, they demonstrated the merits of their proposed algorithm. & [6] \\ \hline Gaining-sharing knowledge-based algorithm & 2020 & [PERSON] et al. proposed a gaining-sharing knowledge-based algorithm and proved it was better by completing experiments on various problems, along with CEC 2017 benchmark functions. & [7] \\ \hline Coronavirus optimization algorithm & 2020 & [PERSON] et al. introduced a novel bio-inspired metaheuristic, based on the coronavirus behavior. They elaborated major advantages of coronavirus optimization algorithm compared to other similar strategies. & [8] \\ \hline Harris Hawks Optimization & 2019 & [PERSON] et al. proposed a novel paradigm called Harris Hawks Optimizer (HHO), and tested it on 29 benchmark problems and several real-world engineering problems & [9] \\ \hline African Buffalo Optimization & 2015 & [PERSON] et al. developed a novel optimization technique, namely the African Buffalo Optimization (ABO) and checked its validation on a number of benchmark Traveling Salesman Problems. Authors recommended to use ABO to solve knapsack problems. & [10] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of some of the swarm intelligence-based algorithms. However, due to the stochastic nature of the optimization problems, it is very difficult to achieve a balance between these components. Success in solving any kind of optimization problem depends upon the selection of an appropriate algorithm. This was also proved by [PERSON] and [PERSON] [12], in their theorem called as the \"no free lunch\" theorem, that no algorithm is perfect to solve every kind of optimization problem. So, the main concept is to choose wisely an appropriate optimization technique to solve a given hand -in optimization problems, with less computational effort and a higher rate of convergence performance.