# An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning
## 4 Experiments and Analyses
### Performance Comparison Experiment and Result Analysis
However, for the 100-dimensional functions \(\mathsf{f_{2}(x)}\) and \(\mathsf{f_{9}(x)}\)  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Function** & **Index** & **CS** & **ICS** & **ASCSA** & **ECS** & **MCSA** & **BASCS** \\ \hline \multirow{2}{*}{\(f_{1}(x)\)} & mean & \(4.23\times 10^{0}\) & \(5.71\times 10^{-3}\) & \(4.63\times 10^{-8}\) & \(3.04\times 10^{-38}\) & \(6.42\times 10^{1}\) & \(8.81\times 10^{-15}\) \\  & std & \(2.29\times 10^{-1}\) & \(1.07\times 10^{-3}\) & \(1.13\times 10^{-8}\) & \(7.47\times 10^{-38}\) & \(3.50\times 10^{1}\) & \(4.73\times 10^{-14}\) \\ \hline \multirow{2}{*}{\(f_{2}(x)\)} & mean & \(3.01\times 10^{2}\) & \(2.06\times 10^{0}\) & \(3.78\times 10^{-4}\) & \(1.69\times 10^{-2}\) & \(8.23\times 10^{-1}\) & \(1.46\times 10^{-7}\) \\  & std & \(2.57\times 10^{2}\) & \(2.17\times 10^{-1}\) & \(1.83\times 10^{-3}\) & \(4.00\times 10^{-2}\) & \(1.92\times 10^{-1}\) & \(7.88\times 10^{-7}\) \\ \hline \multirow{2}{*}{\(f_{3}(x)\)} & mean & \(4.96\times 10^{0}\) & \(1.26\times 10^{0}\) & \(1.99\times 10^{1}\) & \(4.15\times 10^{0}\) & \(5.51\times 10^{0}\) & \(1.38\times 10^{-3}\) \\  & std & \(5.43\times 10^{0}\) & \(5.92\times 10^{-1}\) & \(1.88\times 10^{-2}\) & \(8.54\times 10^{-1}\) & \(1.56\times 10^{0}\) & \(1.16\times 10^{-3}\) \\ \hline \multirow{2}{*}{\(f_{4}(x)\)} & mean & \(7.36\times 10^{2}\) & \(8.14\times 10^{2}\) & \(9.80\times 10^{2}\) & \(1.20\times 10^{2}\) & \(5.90\times 10^{2}\) & \(9.19\times 10^{-5}\) \\  & std & \(4.41\times 10^{1}\) & \(1.82\times 10^{2}\) & \(3.76\times 10^{1}\) & \(1.24\times 10^{1}\) & \(6.13\times 10^{1}\) & \(4.95\times 10^{-4}\) \\ \hline \multirow{2}{*}{\(f_{5}(x)\)} & mean & \(3.95\times 10^{1}\) & \(7.91\times 10^{0}\) & \(1.17\times 10^{2}\) & \(5.10\times 10^{0}\) & \(6.06\times 10^{1}\) & \(4.18\times 10^{-14}\) \\  & std & \(9.67\times 10^{0}\) & \(1.00\times 10^{0}\) & \(7.96\times 10^{0}\) & \(2.08\times 10^{0}\) & \(1.30\times 10^{1}\) & \(1.26\times 10^{-13}\) \\ \hline \multirow{2}{*}{\(f_{6}(x)\)} & mean & \(1.69\times 10^{1}\) & \(5.84\times 10^{0}\) & \(7.64\times 10^{-2}\) & \(8.88\times 10^{-3}\) & \(9.72\times 10^{1}\) & \(6.45\times 10^{-29}\) \\  & std & \(5.70\times 10^{-1}\) & \(1.33\times 10^{0}\) & \(1.80\times 10^{-1}\) & \(2.45\times 10^{-29}\) & \(3.57\times 10^{1}\) & \(3.18\times 10^{-28}\) \\ \hline \multirow{2}{*}{\(f_{7}(x)\)} & mean & \(2.54\times 10^{1}\) & \(1.69\times 10^{-2}\) & \(2.97\times 10^{-1}\) & \(1.94\times 10^{-1}\) & \(1.18\times 10^{2}\) & \(0.00\times 10^{0}\) \\  & std & \(3.01\times 10^{0}\) & \(1.61\times 10^{-3}\) & \(2.96\times 10^{-2}\) & \(4.39\times 10^{-2}\) & \(4.51\times 10^{1}\) & \(0.00\times 10^{0}\) \\ \hline \multirow{2}{*}{\(f_{8}(x)\)} & mean & \(1.22\times 10^{4}\) & \(3.79\times 10^{-3}\) & \(1.61\times 10^{-4}\) & \(9.96\times 10^{-35}\) & \(3.49\times 10^{5}\) & \(3.07\times 10^{-8}\) \\  & std & \(9.70\times 10^{2}\) & \(1.91\times 10^{-4}\) & \(3.41\times 10^{-5}\) & \(1.42\times 10^{-34}\) & \(2.09\times 10^{5}\) & \(1.28\times 10^{-7}\) \\ \hline \multirow{2}{*}{\(f_{9}(x)\)} & mean & \(7.88\times 10^{1}\) & \(2.10\times 10^{-1}\) & \(1.79\times 10^{-3}\) & \(3.20\times 10^{1}\) & \(5.00\times 10^{2}\) & \(6.67\times 10^{-13}\) \\  & std & \(1.54\times 10^{0}\) & \(3.46\times 10^{-1}\) & \(1.27\times 10^{-3}\) & \(4.31\times 10^{0}\) & \(3.45\times 10^{2}\) & \(3.59\times 10^{-12}\) \\ \hline \multirow{2}{*}{\(f_{10}(x)\)} & mean & \(4.82\times 10^{1}\) & \(3.35\times 10^{-1}\) & \(6.98\times 10^{3}\) & \(3.16\times 10^{-2}\) & \(2.70\times 10^{2}\) & \(3.82\times 10^{-8}\) \\  & std & \(3.97\times 10^{1}\) & \(6.67\times 10^{-2}\) & \(3.51\times 10^{3}\) & \(1.11\times 10^{-2}\) & \(1.23\times 10^{2}\) & \(2.03\times 10^{-7}\) \\BASCs ranks first. According to the overall average ranking order, BASCS's optimization performance surpasses that of the others. Figures 12-17 illustrate the convergence trajectories of the six algorithms on unimodal functions across 50 and 100 dimensions.