# A New Fuzzy Harmony Search Algorithm Using Fuzzy Logic for Dynamic Parameter Adaptation
## 5 Comparison between of Fuzzy Harmony Search Algorithm (FHS) and of Fuzzy Bat Algorithm (FBA)

Table 13: Experiment results obtained for the sphere function with FBA and FHS algorithms. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**FBA**} & \multicolumn{3}{c}{**FHS**} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Num.** \\ **Bats** \\ \end{tabular} } & \multirow{2}{*}{**Best**} & \multirow{2}{*}{**Worst**} & \multirow{2}{*}{**Average**} & \multicolumn{2}{c}{**Num.**} & \multirow{2}{*}{**Best**} & \multirow{2}{*}{**Worst**} & \multirow{2}{*}{**Average**} \\  & & & & & & & \\ \hline
**2** & \(3.81\mathrm{E}-05\) & \(9.52\mathrm{E}-01\) & \(4.60\mathrm{E}-01\) & **2** & \(2.64\mathrm{E}-08\) & \(1.67\mathrm{E}-04\) & \(1.94\mathrm{E}-05\) \\
**5** & \(1.07\mathrm{E}-05\) & \(6.69\mathrm{E}-01\) & \(2.23\mathrm{E}-01\) & **5** & \(1.07\mathrm{E}-08\) & \(1.38\mathrm{E}-04\) & \(5.53\mathrm{E}-06\) \\
**10** & \(2.76\mathrm{E}-07\) & \(2.00\mathrm{E}+00\) & \(1.13\mathrm{E}+00\) & **10** & \(8.46\mathrm{E}-08\) & \(4.28\mathrm{E}-04\) & \(1.62\mathrm{E}-05\) \\
**20** & \(4.41\mathrm{E}-07\) & \(3.45\mathrm{E}+00\) & \(9.60\mathrm{E}-01\) & **20** & \(1.98\mathrm{E}-08\) & \(6.08\mathrm{E}-05\) & \(4.05\mathrm{E}-06\) \\
**30** & \(3.19\mathrm{E}-06\) & \(2.05\mathrm{E}+00\) & \(9.22\mathrm{E}-01\) & **30** & \(2.63\mathrm{E}-08\) & \(5.99\mathrm{E}-06\) & \(1.50\mathrm{E}-06\) \\
**40** & \(3.91\mathrm{E}-06\) & \(1.56\mathrm{E}+00\) & \(4.17\mathrm{E}-01\) & **40** & \(1.40\mathrm{E}-08\) & \(9.02\mathrm{E}-05\) & \(1.05\mathrm{E}-05\) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Experiment results obtained for the Ackley function with the FBA and FHS algorithms. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**FBA**} & \multicolumn{3}{c}{**FHS**} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Num.** \\ **Bats** \\ \end{tabular} } & \multirow{2}{*}{**Best**} & \multirow{2}{*}{**Worst**} & \multirow{2}{*}{**Average**} & \multicolumn{2}{c}{**Num.**} & \multirow{2}{*}{**Best**} & \multirow{2}{*}{**Worst**} & \multirow{2}{*}{**Average**} \\  & & & & & & & \\ \hline
**2** & \(0.00\mathrm{E}+00\) & \(5.29\mathrm{E}-01\) & \(1.11\mathrm{E}-01\) & **2** & \(1.00\mathrm{E}-12\) & \(2.80\mathrm{E}-10\) & \(2.38\mathrm{E}-11\) \\
**5** & \(1.67\mathrm{E}-03\) & \(1.71\mathrm{E}+00\) & \(6.60\mathrm{E}-01\) & **5** & \(8.86\mathrm{E}-14\) & \(1.20\mathrm{E}-10\) & \(1.42\mathrm{E}-11\) \\
**10** & \(0.00\mathrm{E}+00\) & \(1.40\mathrm{E}-03\) & \(6.13\mathrm{E}-04\) & **10** & \(4.58\mathrm{E}-13\) & \(6.50\mathrm{E}-11\) & \(1.12\mathrm{E}-11\) \\
**20** & \(1.83\mathrm{E}-02\) & \(1.00\mathrm{E}+00\) & \(5.09\mathrm{E}-01\) & **20** & \(2.33\mathrm{E}-13\) & \(8.56\mathrm{E}-11\) & \(1.85\mathrm{E}-11\) \\
**30** & \(2.39\mathrm{E}-02\) & \(8.69\mathrm{E}-01\) & \(5.31\mathrm{E}-01\) & **30** & \(2.33\mathrm{E}-13\) & \(8.56\mathrm{E}-11\) & \(1.85\mathrm{E}-11\) \\
**40** & \(3.32\mathrm{E}-02\) & \(1.00\mathrm{E}+00\) & \(3.94\mathrm{E}-01\) & **40** & \(5.44\mathrm{E}-14\) & \(1.35\mathrm{E}-10\) & \(1.96\mathrm{E}-11\) \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Experiment results obtained for the Rosenbrock function with the FBA and FHS algorithms. In Table 17, the overall averages for each method according to the number of bats or harmonies used can be found, as well as the best and the worst results obtained for the Rastrigin function. In Table 18, the overall averages for each method according to the number of bats or harmonies used can be observed, as well as the best and the worst results obtained for the sum squared function.