# Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem
## 5 Experimental Studies
### Results of unimodal functions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Metric** & **ETLBO** & **TLBO** & **TLBO(S)** & **PSO** & **DE** & **GA** \\ \hline \multirow{4}{*}{F1} & Best & \(\mathbf{1.41\times 10^{-134}}\) & \(1.11\times 10^{-11}\) & \(7.31\times 10^{-20}\) & \(1.44\times 10^{01}\) & \(4.61\times 10^{00}\) & \(4.07\times 10^{02}\) \\  & Worst & \(\mathbf{3.35\times 10^{-129}}\) & \(1.47\times 10^{-11}\) & \(2.81\times 10^{-11}\) & \(1.59\times 10^{02}\) & \(6.83\times 10^{00}\) & \(9.34\times 10^{02}\) \\  & Median & \(\mathbf{1.92\times 10^{-132}}\) & \(1.40\times 10^{-11}\) & \(1.72\times 10^{-14}\) & \(1.59\times 10^{01}\) & \(5.73\times 10^{00}\) & \(5.59\times 10^{02}\) \\  & Average & \(\mathbf{1.19\times 10^{-130}}\) & \(1.32\times 10^{-11}\) & \(1.16\times 10^{-12}\) & \(2.79\times 10^{01}\) & \(5.72\times 10^{00}\) & \(5.65\times 10^{02}\) \\  & SD & \(\mathbf{5.04\times 10^{-130}}\) & \(8.61\times 10^{-13}\) & \(4.21\times 10^{-12}\) & \(3.36\times 10^{01}\) & \(5.11\times 10^{-01}\) & \(9.93\times 10^{01}\) \\ \hline \multirow{4}{*}{F2} & Best & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(2.70\times 10^{-23}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) \\  & Worst & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(4.46\times 10^{-15}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) \\  & Median & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(1.90\times 10^{-20}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) \\  & Average & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(9.13\times 10^{-17}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) \\  & SD & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(6.23\times 10^{-10}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) \\ \hline \multirow{4}{*}{F3} & Best & \(\mathbf{1.29\times 10^{-07}}\) & \(4.80\times 10^{-01}\) & \(7.70\times 10^{-05}\) & \(2.45\times 10^{-02}\) & \(2.50\times 10^{-02}\) & \(1.43\times 10^{-02}\) \\  & Worst & \(\mathbf{1.75\times 10^{-04}}\) & \(6.76\times 10^{-01}\) & \(1.01\times 10^{-02}\) & \(5.40\times 10^{-02}\) & \(8.61\times 10^{-02}\) & \(1.13\times 10^{-01}\) \\  & Median & \(\mathbf{2.95\times 10^{-05}}\) & \(6.17\times 10^{-01}\) & \(1.41\times 10^{-03}\) & \(5.23\times 10^{-05}\) & \(5.20\times 10^{-02}\) & \(3.90\times 10^{-02}\) \\  & Average & \(\mathbf{4.40\times 10^{-05}}\) & \(6.12\times 10^{-01}\) & \(2.15\times 10^{-03}\) & \(8.61\times 10^{-02}\) & \(5.54\times 10^{-02}\) & \(4.22\times 10^{-02}\) \\  & SD & \(\mathbf{3.85\times 10^{-05}}\) & \(3.66\times 10^{-02}\) & \(1.86\times 10^{-03}\) & \(1.30\times 10^{-02}\) & \(1.48\times 10^{-02}\) & \(1.84\times 10^{-02}\) \\ \hline \multirow{4}{*}{F4} & Best & \(\mathbf{0.00\times 10^{00}}\) & \(4.72\times 10^{-09}\) & \(1.00\times 10^{-16}\) & \(2.19\times 10^{-01}\) & \(1.00\times 10^{01}\) & \(1.18\times 10^{01}\) \\  & Worst & \(\mathbf{0.00\times 10^{00}}\) & \(6.11\times 10^{-09}\) & \(2.62\times 10^{-13}\) & \(1.56\times 10^{00}\) & \(1.61\times 10^{01}\) & \(3.07\times 10^{01}\) \\  & Median & \(\mathbf{0.00\times 10^{00}}\) & \(5.37\times 10^{-09}\) & \(1.60\times 10^{-14}\) & \(7.17\times 10^{-01}\) & \(1.38\times 10^{01}\) & \(1.88\times 10^{01}\) \\  & Average & \(\mathbf{0.00\times 10^{00}}\) & \(5.35\times 10^{-09}\) & \(3.57\times 10^{-14}\) & \(8.01\times 10^{-01}\) & \(1.35\times 10^{01}\) & \(1.97\times 10^{01}\) \\  & SD & \(\mathbf{0.00\times 10^{00}}\) & \(3.27\times 10^{-10}\) & \(5.11\times 10^{-14}\) & \(3.82\times 10^{-01}\) & \(1.38\times 10^{00}\) & \(3.76\times 10^{00}\) \\ \hline \multirow{4}{*}{F5} & Best & \(\mathbf{0.00\times 10^{00}}\) & \(2.00\times 10^{00}\) & \(8.37\times 10^{-08}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(2.07\times 10^{-03}\) \\  & Worst & \(2.15\times 10^{-04}\) & \(2.00\times 10^{00}\) & \(2.30\times 10^{-01}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(1.91\times 10^{00}\) \\  & Median & \(3.74\times 10^{-16}\) & \(2.00\times 10^{00}\) & \(1.64\times 10^{-02}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(6.00\times 10^{-01}\) \\  & Average & \(8.05\times 10^{-06}\) & \(2.00\times 10^{00}\) & \(2.30\times 10^{-02}\) & \(\mathbf{0.00\times 10^{00}}\) & \(\mathbf{0.00\times 10^{00}}\) & \(9.45\times 1 Based on the above presentations, it can be concluded that the proposed ETLBO performed better and obtained more consistent accurate solutions for most unimodal functions.