# Dynamic Weight and Mapping Mutation Operation-Based Salp Swarm Algorithm for Global Optimization
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Fun** & **Result** & **SSA** & **MFO** & **GOA** & **ALO** & **WMSSA** \\ \hline F1 & Mean & \(1.27\times 10^{-08}\) & \(2.67\times 10^{+03}\) & \(9.41\times 10^{+01}\) & \(9.18\times 10^{-06}\) & \(\mathbf{1.76\times 10^{-22}}\) \\  & Std & \(2.49\times 10^{-09}\) & \(5.83\times 10^{+03}\) & \(9.79\times 10^{+01}\) & \(8.37\times 10^{-06}\) & \(\mathbf{7.22\times 10^{-22}}\) \\ F2 & Mean & \(1.73\times 10^{-04}\) & \(1.00\times 10^{+00}\) & \(2.28\times 10^{+00}\) & \(1.17\times 10^{-01}\) & \(\mathbf{2.02\times 10^{-14}}\) \\  & Std & \(9.00\times 10^{-04}\) & \(3.05\times 10^{+00}\) & \(6.63\times 10^{+00}\) & \(5.05\times 10^{-01}\) & \(\mathbf{5.09\times 10^{-14}}\) \\ F3 & Mean & \(2.05\times 10^{-09}\) & \(1.67\times 10^{+02}\) & \(4.63\times 10^{-02}\) & \(7.84\times 10^{-06}\) & \(\mathbf{5.27\times 10^{-21}}\) \\  & Std & \(9.87\times 10^{-10}\) & \(9.13\times 10^{+02}\) & \(9.24\times 10^{-02}\) & \(1.02\times 10^{-05}\) & \(\mathbf{1.62\times 10^{-20}}\) \\ F4 & Mean & \(1.58\times 10^{-05}\) & \(5.84\times 10^{-01}\) & \(6.50\times 10^{-03}\) & \(1.18\times 10^{-04}\) & \(\mathbf{2.22\times 10^{-14}}\) \\  & Std & \(3.69\times 10^{-06}\) & \(1.87\times 10^{+00}\) & \(1.73\times 10^{-02}\) & \(1.36\times 10^{-04}\) & \(\mathbf{5.96\times 10^{-15}}\) \\ F5 & Mean & \(5.17\times 10^{-10}\) & \(\mathbf{1.23\times 10^{-30}}\) & \(1.89\times 10^{-08}\) & \(2.57\times 10^{-09}\) & \(1.22\times 10^{-27}\) \\  & Std & \(2.08\times 10^{-10}\) & \(\mathbf{2.38\times 10^{-30}}\) & \(9.99\times 10^{-09}\) & \(9.10\times 10^{-10}\) & \(4.31\times 10^{-28}\) \\ F6 & Mean & \(-2.78\times 10^{+03}\) & \(-3.21\times 10^{+03}\) & \(-2.54\times 10^{+03}\) & \(-2.30\times 10^{+03}\) & \(-4.19\times 10^{+03}\) \\  & Std & \(3.51\times 10^{+02}\) & \(3.57\times 10^{+02}\) & \(3.89\times 10^{+02}\) & \(3.48\times 10^{+02}\) & \(\mathbf{1.95\times 10^{-12}}\) \\ F7 & Mean & \(1.66\times 10^{+01}\) & \(2.16\times 10^{+01}\) & \(4.54\times 10^{+01}\) & \(2.16\times 10^{+01}\) & \(\mathbf{4.74\times 10^{-16}}\) \\  & Std & \(6.19\times 10^{+00}\) & \(1.41\times 10^{+01}\) & \(2.31\times 10^{+01}\) & \(1.03\times 10^{+01}\) & \(\mathbf{2.59\times 10^{-15}}\) \\ F8 & Mean & \(7.34\times 10^{-01}\) & \(1.95\times 10^{+00}\) & \(1.85\times 10^{+00}\) & \(1.32\times 10^{-01}\) & \(\mathbf{1.39\times 10^{-14}}\) \\  & Std & \(9.40\times 10^{-01}\) & \(5.66\times 10^{+00}\) & \(4.43\times 10^{+00}\) & \(4.09\times 10^{-01}\) & \(\mathbf{2.69\times 10^{-15}}\) \\ F9 & Mean & \(2.18\times 10^{-01}\) & \(1.66\times 10^{-01}\) & \(2.19\times 10^{-01}\) & \(7.46\times 10^{-01}\) & \(\mathbf{5.96\times 10^{-29}}\) \\  & Std & \(4.17\times 10^{-01}\) & \(2.91\times 10^{-01}\) & \(4.34\times 10^{-01}\) & \(1.23\times 10^{+00}\) & \(\mathbf{5.83\times 10^{-29}}\) \\ F10 & Mean & \(7.32\times 10^{-04}\) & \(1.50\times 10^{-05}\) & \(2.90\times 10^{-03}\) & \(1.10\times 10^{-03}\) & \(\mathbf{2.27\times 10^{-28}}\) \\  & Std & \(2.80\times 10^{-03}\) & \(3.80\times 10^{-03}\) & \(5.60\times 10^{-03}\) & \(3.40\times 10^{-03}\) & \(\mathbf{1.78\times 10^{-28}}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison with other swarm-based optimization algorithms. **Funding:** This paper is supported by the National Natural Science Foundation of China (61873246, 62006213), the Zhongyuan Science and Technology Innovation Leadership Program (214200510026), the Program for Science and Technology Innovation Talents in Universities of Henan Province (21 HASITIT028), and the Natural Science Foundation of Henan (202300410495). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Any research article describing a study involving humans should contain this statement. **Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.