# A Novel Optimization Algorithm Combing
## 4 Experiments
### Effects of Each Improvement Measure on the Performance of GABCG
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{**CZ**} & \multicolumn{2}{c}{**ABC**} & \multicolumn{2}{c}{**GABC**} & \multicolumn{2}{c}{**GABCG**} \\ \cline{3-8}  & & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline \multirow{4}{*}{Rosenbrock} & 80 & \(6.15\times 10^{-1}\) & \(3.28\times 10^{-1}\) & \(5.45\times 10^{-2}\) & \(3.65\times 10^{-2}\) & \(\mathbf{3.16\times 10^{-2}}\) & \(3.71\times 10^{-2}\) \\  & 160 & \(2.33\times 10^{-1}\) & \(9.72\times 10^{-2}\) & \(4.98\times 10^{-2}\) & \(3.29\times 10^{-2}\) & \(\mathbf{2.20\times 10^{-2}}\) & \(2.56\times 10^{-2}\) \\  & 240 & \(1.65\times 10^{-1}\) & \(1.80\times 10^{-1}\) & \(2.94\times 10^{-2}\) & \(1.78\times 10^{-2}\) & \(\mathbf{1.12\times 10^{-2}}\) & \(1.50\times 10^{-2}\) \\  & 80 & \(7.22\times 10^{-3}\) & \(7.63\times 10^{-3}\) & \(9.89\times 10^{-15}\) & \(1.57\times 10^{-14}\) & \(\mathbf{1.21\times 10^{-15}}\) & \(7.63\times 10^{-16}\) \\  & 160 & \(1.04\times 10^{-4}\) & \(6.44\times 10^{-5}\) & \(1.83\times 10^{-15}\) & \(3.44\times 10^{-16}\) & \(\mathbf{6.50\times 10^{-16}}\) & \(8.46\times 10^{-17}\) \\  & 240 & \(2.47\times 10^{-5}\) & \(1.93\times 10^{-5}\) & \(1.74\times 10^{-15}\) & \(2.75\times 10^{-16}\) & \(\mathbf{5.27\times 10^{-16}}\) & \(6.98\times 10^{-17}\) \\  & 80 & \(4.24\times 10^{-1}\) & \(2.59\times 10^{-2}\) & \(2.87\times 10^{-1}\) & \(4.19\times 10^{-2}\) & \(\mathbf{2.51\times 10^{-1}}\) & \(3.61\times 10^{-2}\) \\  & 160 & \(3.95\times 10^{-1}\) & \(1.81\times 10^{-2}\) & \(2.41\times 10^{-1}\) & \(3.12\times 10^{-2}\) & \(\mathbf{2.30\times 10^{-1}}\) & \(5.60\times 10^{-2}\) \\  & 240 & \(3.36\times 10^{-1}\) & \(3.51\times 10^{-2}\) & \(1.88\times 10^{-1}\) & \(3.96\times 10^{-2}\) & \(\mathbf{1.73\times 10^{-1}}\) & \(4.41\times 10^{-2}\) \\  & 80 & \(1.27\times 10^{-5}\) & \(4.53\times 10^{-5}\) & \(6.68\times 10^{-12}\) & \(1.04\times 10^{-11}\) & \(\mathbf{1.14\times 10^{-14}}\) & \(3.47\times 10^{-14}\) \\ Rastrigin & 160 & \(5.29\times 10^{-7}\) & \(2.86\times 10^{-6}\) & \(1.87\times 10^{-12}\) & \(2.09\times 10^{-12}\) & \(\mathbf{7.58\times 10^{-15}}\) & \(3.60\times 10^{-14}\) \\  & 240 & \(2.92\times 10^{-9}\) & \(7.81\times 10^{-9}\) & \(7.39\times 10^{-13}\) & \(2.53\times 10^{-13}\) & \(3.79\times 10^{-15}\) & \(2.08\times 10^{-14}\) \\  & 80 & \(8.62\times 10^{-11}\) & \(4.55\times 10^{-10}\) & \(2.20\times 10^{-14}\) & \(5.17\times 10^{-14}\) & \(\mathbf{6.66\times 10^{-17}}\) & \(1.29\times 10^{-16}\) \\ Griewank & 160 & \(1.83\times 10^{-14}\) & \(2.15\times 10^{-14}\) & \(1.11\times 10^{-15}\) & \(5.56\times 10^{-16}\) & \(\mathbf{5.55\times 10^{-17}}\) & \(5.85\times 10^{-17}\) \\  & 240 & \(1.22\times 10^{-14}\) & \(9.87\times 10^{-15}\) & \(6.66\times 10^{-16}\) & \(2.09\times 10^{-16}\) & \(\mathbf{4.44\times 10^{-17}}\) & \(5.73\times 10^{-17}\) \\ \hline \end{tabular} Mean and SD donate the average and standard deviation of optimization results in 30 runs, and the best mean results are in bold.  
\end{table} 
Table 4: Optimization results of the ABC, GABC, and GABCG algorithms with different colony sizes. Figure 5: Convergence curves of different GABC algorithms for Rastrigin and Griewank functions.