## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Proposed Method** & **Enhancement** & **Limitation** \\ \hline \multirow{3}{*}{[20]} & \multirow{3}{*}{Modified SCA} & (a) improved version of SCA with consideration of OBI. & \multirow{3}{*}{(a) unable to solve the dimension problem.} \\  & & & (b) increased accuracy of the optimal solution and ability to explore the search space in optimization process. & \\ \hline \multirow{3}{*}{[21]} & \multirow{3}{*}{ISCA by modified} & (a) modified the position updating weight to balance the exploration and exploitation of SCA. & \multirow{3}{*}{(a) means of CPU times of ISCA were small values.} \\  & & & (b) modified the conversion parameter for decreasing the non-linear strategy based on Gaussian function. & \\ \hline \multirow{3}{*}{[29]} & \multirow{3}{*}{Hybrid GWO-SCA} & (a) improved by using position update equations of SCA. & \multirow{3}{*}{(a) the high number of dimensions is worse than in the WOA and TLBO algorithm.} \\  & & & (b) highest capability to avoid the local optima and considerably superior to other metaheuristics. & \\ \hline \multirow{3}{*}{[18]} & \multirow{3}{*}{SCA\(-\)Multi-Orthogonal} & Hybrid & (a) enhanced exploration capability. & \multirow{3}{*}{(a) immature convergence inaccuracy phase for some benchmark functions.} \\  & & & (b) boosts exploitation reaches. & \\  & & & (c) robust, statistically sound and quick convergence. & \\ \hline \multirow{3}{*}{[19]} & \multirow{3}{*}{ISCA with new updating mechanism} & (a) improved exploration and exploitation based on nonlinear strategy to find the algorithm strength. & \multirow{3}{*}{(a) not all selected functions are minimized, and ISCA shows instability in percentage performance compared to other optimization methods.} \\  & & & (b) proposed algorithm able to get away from local optima & \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{Hybrid SCA-PSO} & (a) good performance in accuracy and computational time & \multirow{3}{*}{(a) poor exploitation in finding the longest consecutive substrings.} \\  & & & (b) improves the convergence and the tendency. & \multirow{3}{*}{(b) not all the test function provided minimum standard deviation.} \\ \hline \multirow{3}{*}{[24]} & \multirow{3}{*}{Modified SCA} & (a) improved SCA through three optimization techniques by decreasing the conversion parameter and inertia weight, using random optimal individuals, and greedy Levy mutation techniques. & \multirow{3}{*}{(a) still in infancy stage, and the complexity was greatly increased.} \\  & & & (b) effectively avoids becoming trapped in a local optimum; has faster convergence; has higher optimization accuracy. Twenty benchmark test functions were applied to verify the performance. & \\ \hline \multirow{3}{*}{[20]} & \multirow{3}{*}{Hybrid SCA-DE} & (a) solved the optimization problem and object tracking. & \multirow{3}{*}{(a) SCA does not lead to optimal solutions to complex problems when used on some benchmark functions.} \\  & & & (b) does not compare each of the independent runs (chance of low probability). & \\ \hline \multirow{3}{*}{[24]} & \multirow{3}{*}{Hybrid SCA-PSO} & (a) good performance method in exploiting the optimum and has advantages in exploration. & \multirow{3}{*}{(a) did not guarantee an accurate convergence detection.} \\  & & & (a) allows the design to be searched further to find & \\ \hline \multirow{3}{*}{[25]} & \multirow{3}{*}{Hybrid PSO-SCA and Levy flight} & (a) allows the design to be searched further to find & \multirow{3}{*}{(a) not all stages of the optimization in convergence} \\  & & & (b) easily convergence and trapped in local minimum. \\ \hline \multirow{3}{*}{[16]} & \multirow{3}{*}{Improved SCA with existing strategy and new updating mechanism} & (a) to improve SCA accuracy by selecting the best features using the elitism strategy and new solution. & \multirow{3}{*}{(a) immature convergence curve for minimization fitness features.} \\  & & & (b) solving three constrained real engineering & \\ \hline \multirow{3}{*}{[22]} & \multirow{3}{*}{Modified SCA} & (a) local optima with faster convergence. & \multirow{3}{*}{(a) immature convergence.} \\  & & & (b) does not affect the configuration of the original SCA. \\ \hline \hline \end{tabular}
\end{table}