## 1 Introduction  
The purpose of optimization is to determine the best solution among the available solutions for an optimization problem according to the constraints of problem [1]. Each optimization problem is designed with three parts: constraints, objective functions, and decision variables [2]. There are many optimization problems in different sciences that should be optimized using the appropriate method. Stochastic search-based optimization algorithms have always been of interest to researchers in solving optimization problems [3]. Optimization algorithms are able to provide a quasi-optimal solution based on random scan of the search space instead of a full scan. The quasi-optimal solution is not the best solution, but it is close to the global optimal solution of the problem [1]. In this regard, optimization algorithms have been applied by scientists in various fields such as energy [4; 5; 6], protection [7], electrical engineering [8; 9; 10; 11; 12; 13], topology optimization [14] and energy carriers [15; 16; 17] to achieve the quasi-optimal solution. Table 1 shows the optimization algorithms grouped according to the main design idea. \begin{table}
\begin{tabular}{c c} \hline \hline  & Ref. [18] The most widely used \\  & & algorithm in this group, which is \\ \(\bullet\) & Particle Swarm Optimization & designed based on modeling the \\  & (PSO) [18], Ant Colony & movement of birds. \\  & Optimization (ACO) [19; 20], & Refs. [19; 20] It is based on the \\  & Spotted Hyena Optimizer & modeling the discovery of the \\  & (SHO) [21], Group Optimization & shortest path by ants. \\  & (GO) [22], Artificial Bee Colony & Ref. [29] It has gained wide \\  & (ABC) [23], Following & acceptance among the \\  & Optimization Algorithm & optimization researchers. \\  & (FOA) [24], Rat Swarm & This algorithm is a \\  & Optimizer (RSO) [2], Multi & teaching-learning process inspired \\  & Leader Optimizer (MLO) [1], & algorithm and is based on the \\  & Bat-inspired Algorithm (BA) [25], & effect of influence of a teacher on \\  & Emperor Penguin Optimizer & the output of learners in a class. \\  & (EPO) [26], Cukoko Search & Ref. [31] It is designed by \\ Optimization & (CS) [27], Donkey Theorem & simulating the process of treating \\ Algorithms & Optimization (DTO) [28], & patients by a physician. \\  & Teaching-Learning-Based & The treatment process has three \\  & Optimization (TLBO) & phases, including vaccination, \\  & Algorithm [29], Grasshopper & drug administration, and surgery. \\  & Optimization Algorithm & Ref. [32] It mimics the leadership \\  & (GOA) [30], Doctor and Patient & hierarchy and hunting mechanism \\  & Optimization (DPO) [31], Gray & of grey wolves in nature. Four \\  & Wolf Optimizer (GWO) [32] & types of grey wolves such as \\  & & alpha, beta, delta, and omega are \\  & & employed for simulating the \\  & & leadership hierarchy. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Optimization algorithms. Each optimization problem has a definite solution called a global solution. Optimization algorithms provide a solution based on random search of the search space, which is not necessarily a universal solution, but because it is close to the optimal solution, it is an acceptable solution. The solution that is provided by optimization algorithms is called quasi-optimal solution. Therefore, an optimization algorithm that offers a better quasi-optimal solution than another algorithm is a better optimizer algorithm. In this regard, many optimization algorithms have been proposed by researchers to solve optimization problems and achieve to the better quasi-optimal solution. Although optimization algorithms have been successful in solving many optimization problems, improving the equations of optimization algorithms and adding modification phases to optimization algorithms can lead to better quasi-optimal solutions.