# Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization
## 4 Experimental Results and Discussions
### Analysis of CFA Strategies
#### 4.2.2 Adaptive Strategy for Local Search  
Local search is a rudimentary component contained in most of the modern metaheuristic algorithms. The local search procedure exploits the regional function profiles and makes the master metaheuristic algorithm more effective than the original form which does not embed this procedure. However, the local search procedure can be computationally expensive if it is performed within each iteration of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Functions**} & \multicolumn{2}{c}{**Roulette-Wheel Selection**} & \multicolumn{2}{c}{**Tournament Selection**} & \multicolumn{2}{c}{**Rank Selection**} \\ \cline{2-7}  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline [PERSON] (2) & \(-1.0\) & \(0.0\) & \(-1.0\) & \(0.0\) & \(-1.0\) & \(0.0\) \\ [PERSON] (2) & \(-186.726\) & \(0.011381\) & \(-186.723\) & \(0.010061\) & \(-186.723\) & \(0.016331\) \\ [PERSON] (2) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) \\ [PERSON] (2) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) \\ [PERSON] (3) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) \\ [PERSON] (4, 5) & \(-8.74128\) & \(2.436384\) & \(-8.64565\) & \(2.5462\) & \(-8.09613\) & \(3.003044\) \\ [PERSON] (4, 7) & \(-9.91291\) & \(1.345608\) & \(-10.1835\) & \(1.11003\) & \(-10.2611\) & \(0.440954\) \\ [PERSON] (4, 10) & \(-10.532\) & \(0.013263\) & \(-10.511\) & \(0.130888\) & \(-10.3554\) & \(0.966638\) \\ [PERSON] (10) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) \\ [PERSON] (10) & \(0.000052\) & \(0.000031\) & \(0.000206\) & \(0.000982\) & \(0.002511\) & \(0.009392\) \\ [PERSON] (10) & \(0.851670\) & \(0.878212\) & \(0.847894\) & \(0.930433\) & \(0.646338\) & \(0.800856\) \\ [PERSON] (10) & \(0.023954\) & \(0.015559\) & \(0.018670\) & \(0.010681\) & \(0.016450\) & \(0.009914\) \\ [PERSON] (10) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) \\ [PERSON] (20) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) \\ [PERSON] (20) & \(0.800503\) & \(1.594902\) & \(0.953237\) & \(1.679062\) & \(0.267050\) & \(0.994099\) \\ [PERSON] (20) & \(3.052760\) & \(1.833682\) & \(2.607486\) & \(1.544556\) & \(2.466102\) & \(1.765681\) \\ [PERSON] (20) & \(0.013792\) & \(0.020267\) & \(0.011079\) & \(0.010911\) & \(0.007213\) & \(0.012479\) \\ [PERSON] (20) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) \\ [PERSON] (30) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) \\ [PERSON] (30) & \(5.924423\) & \(3.057484\) & \(2.962683\) & \(2.773249\) & \(0.801094\) & \(1.598004\) \\ [PERSON] (30) & \(4.204611\) & \(2.066075\) & \(4.973816\) & \(1.826780\) & \(4.680690\) & \(2.632701\) \\ [PERSON] (30) & \(0.009349\) & \(0.012921\) & \(0.008202\) & \(0.012808\) & \(0.003364\) & \(0.007581\) \\ [PERSON] (30) & \(0.000005\) & \(0.000005\) & \(0.0\) & \(0.0\) & \(0.0\) & \(0.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mean best function value and the standard deviation obtained by using various selection strategies.  
the master metaheuristic algorithm. As previously noted, the CFA applies the adaptive local search strategy which dynamically varies the frequency of the local search execution according to the result of the landscape analysis. Table 4 tabulates the mean best function value and the standard deviation obtained by CFA with or without adaptive local search. We observe that for all the benchmark test functions the CFA with adaptive local search obtains better or equivalent mean function value than its counterpart without adaptive local search.