# A Modified Artificial Bee Colony Algorithm Based on the Self-Learning Mechanism
## 6 Conclusions  
This paper proposes an improved ABC algorithm based on the self-learning mechanism with five SSEs as the candidate operator pool. Among them, one SSE is good at exploration; other two SSEs are good at exploitation; another SSE intends to balance the two aspects. The last SSE with Levy flight step-size can avoid trapping in local optimal solution. Meanwhile, a simple self-learning mechanism is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Functions** & **Metrics** & **SLABC1** & **SLABC2** & **SLABC3** & **SLABC4** & **SLABC** \\ \hline \multirow{4}{*}{\(f_{1}\)} & Best & \(3.64\times 10^{-30}\) & \(2.41\times 10^{-25}\) & \(2.53\times 10^{-31}\) & \(2.35\times 10^{-30}\) & \(1.99\times 10^{-29}\) \\  & Median & \(7.91\times 10^{-29}\) & \(1.67\times 10^{-24}\) & \(1.27\times 10^{-30}\) & \(9.15\times 10^{-30}\) & \(8.70\times 10^{-29}\) \\  & Worst & \(1.45\times 10^{-27}\) & \(8.14\times 10^{-24}\) & \(1.85\times 10^{-29}\) & \(3.80\times 10^{-29}\) & \(4.59\times 10^{-28}\) \\  & Mean & \(2.22\times 10^{-28}\) & \(2.10\times 10^{-24}\) & \(4.10\times 10^{-30}\) & \(1.18\times 10^{-29}\) & \(1.25\times 10^{-28}\) \\  & Std & \(3.80\times 10^{-28}\) & \(2.04\times 10^{-24}\) & \(5.43\times 10^{-30}\) & \(9.38\times 10^{-30}\) & \(1.06\times 10^{-28}\) \\ \hline \multirow{4}{*}{\(f_{2}\)} & Best & \(1.40\times 10^{-60}\) & \(1.16\times 10^{-54}\) & \(1.47\times 10^{-63}\) & \(9.24\times 10^{-63}\) & \(1.79\times 10^{-61}\) \\  & Median & \(2.55\times 10^{-59}\) & \(3.53\times 10^{-53}\) & \(3.48\times 10^{-62}\) & \(1.21\times 10^{-61}\) & \(5.17\times 10^{-60}\) \\  & Worst & \(2.09\times 10^{-57}\) & \(8.46\times 10^{-52}\) & \(1.04\times 10^{-60}\) & \(3.52\times 10^{-60}\) & \(2.17\times 10^{-58}\) \\  & Mean & \(3.02\times 10^{-58}\) & \(8.29\times 10^{-53}\) & \(1.48\times 10^{-61}\) & \(6.99\times 10^{-61}\) & \(2.75\times 10^{-59}\) \\  & Std & \(5.46\times 10^{-58}\) & \(1.85\times 10^{-52}\) & \(2.55\times 10^{-61}\) & \(1.15\times 10^{-60}\) & \(5.94\times 10^{-59}\) \\ \hline \multirow{4}{*}{\(f_{3}\)} & Best & \(5.20\times 10^{-16}\) & \(1.49\times 10^{-13}\) & \(6.73\times 10^{-17}\) & \(2.14\times 10^{-16}\) & \(3.23\times 10^{-16}\) \\  & Median & \(7.69\times 10^{-16}\) & \(2.31\times 10^{-13}\) & \(1.80\times 10^{-16}\) & \(3.96\times 10^{-16}\) & \(6.49\times 10^{-16}\) \\  & Worst & \(1.83\times 10^{-15}\) & \(3.98\times 10^{-13}\) & \(3.25\times 10^{-16}\) & \(6.87\times 10^{-16}\) & \(1.24\times 10^{-15}\) \\  & Mean & \(8.93\times 10^{-16}\) & \(2.49\times 10^{-13}\) & \(1.78\times 10^{-16}\) & \(4.14\times 10^{-16}\) & \(6.75\times 10^{-16}\) \\  & Std & \(3.85\times 10^{-16}\) & \(7.91\times 10^{-14}\) & \(7.65\times 10^{-17}\) & \(1.26\times 10^{-16}\) & \(2.71\times 10^{-16}\) \\ \hline \multirow{4}{*}{\(f_{4}\)} & Best & \(1.00\times 10^{-3}\) & \(1.40\times 10^{-3}\) & \(4.52\times 10^{-2}\) & \(2.54\times 10^{-4}\) & \(4.53\times 10^{-2}\) \\  & Median & \(2.91\times 10^{0}\) & \(2.37\times 10^{0}\) & \(5.14\times 10^{0}\) & \(2.12\times 10^{0}\) & \(2.48\times 10^{0}\) \\  & Worst & \(7.04\times 10^{1}\) & \(1.06\times 10^{2}\) & \(1.43\times 10^{2}\) & \(8.68\times 10^{1}\) & \(8.20\times 10^{1}\) \\  & Mean & \(8.10\times 10^{0}\) & \(1.84\times 10^{1}\) & \(3.30\times 10^{1}\) & \(1.87\times 10^{1}\) & \(1.07\times 10^{1}\) \\  & Std & \(1.57\times 10^{1}\) & \(3.05\times 10^{1}\) & \(4.12\times 10^{1}\) & \(3.00\times 10^{1}\) & \(2.43\times 10^{1}\) \\ \hline \multirow{4}{*}{\(f_{5}\)} & Best & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) \\  & Median & \(0.00\times 10^{0}\) & \(0.  
proposed, wherein the SSE is selected according to the previous success ratio in generating promising solutions at each iteration. Experiments verify that the proposed SLABC algorithm can improve search efficiency and speed up the convergence rate. **Author Contributions:** [PERSON] conceived the experiments and wrote most of the paper. [PERSON] performed the experiments and provided funding.