# Adaptive Mutation Dynamic Search Fireworks Algorithm
## 4 Simulation Results and Analysis
### Simulation Results and Analysis
#### 4.2.2 Comparison of AMdynFWA with FWA-Based Algorithms  
To assess the performance of AMdynFWA, AMdynFWA is compared with enhanced fireworks algorithm (EFWA), dynamic search fireworks algorithms (dynFWA), and adaptive fireworks algorithm (AFWA), and the EFWA parameters are set in accordance with [2], the AFWA parameters are set in accordance with [3], and the dynFWA parameters are set in accordance with [4]. The probability \(p\) used in AMdynFWA is set to 0.3. For each test problem, each algorithm runs 51 times, all experimental test function dimensions are set as 30, and their mean errors and total number of rank 1 are reported in Table 3.  
The results from Table 3 indicate that the total number of rank 1 of AMdynFWA (23) is the best of the four algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Functions**} & \multicolumn{1}{c}{\(p\) = 0.1} & \(p\) = 0.3 & \(p\) = 0.5 & \(p\) = 0.7 & \(p\) = 0.9 \\ \cline{2-6}  & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** \\ \hline f1 & \(-\)**1400** & \(-\)**1400** & \(-\)**1400** & \(-\)**1400** & \(-\)**1400** \\ f2 & \(\mathbf{3.76\times 10^{5}}\) & \(3.84\times 10^{5}\) & \(4.56\times 10^{5}\) & \(3.96\times 10^{5}\) & \(4.13\times 10^{5}\) \\ f3 & \(1.01\times 10^{8}\) & \(8.32\times 10^{7}\) & \(\mathbf{5.56\times 10^{7}}\) & \(7.16\times 10^{7}\) & \(6.69\times 10^{7}\) \\ f4 & \(-\)1099.9872 & \(-\)1099.98 & \(-\)**1099.988** & \(-\)1099.9870 & \(-\)1099.984 \\ f5 & \(-\)1000 & \(-\)1000 & \(-\)1000 & \(-\)1000 & \(-\)**1000** \\ f6 & \(-\)870.38 & \(-\)**876.05** & \(-\)875.5 & \(-\)875.29 & \(-\)874.71 \\ f7 & \(-\)713.59 & \(-\)711.45 & \(-\)**713.69** & \(-\)712.66 & \(-\)702.99 \\ f8 & \(-\)**679.069** & \(-\)679.057 & \(-\)679.052 & \(-\)679.063 & \(-\)679.067 \\ f9 & \(-\)**578.503** & \(-\)577.189 & \(-\)577.75 & \(-\)577.436 & \(-\)576.518 \\ f10 & \(-\)**499.976** & \(-\)499.968 & \(-\)499.968 & \(-\)499.972 & \(-\)499.974 \\ f11 & \(-\)305.44 & \(-\)302.436 & \(-\)307.02 & \(-\)**311.215** & \(-\)309.596 \\ f12 & \(-\)164.688 & \(-\)**174.843** & \(-\)163.865 & \(-\)173.722 & \(-\)154.561 \\ f13 & \(-\)31.9988 & \(-\)**36.4318** & \(-\)35.7453 & \(-\)30.6652 & \(-\)32.3421 \\ f14 & 2616.647 & **2543.716** & 2676.641 & 2586.064 & 2704.535 \\ f15 & **3664.113** & 3974.245 & 3888.197 & 3946.214 & 3723.16 \\ f16 & 200.3942 & 200.3496 & 200.3884 & **200.3441** & 300.3698 \\ f17 & 437.5601 & 425.8707 & 426.4633 & **424.32** & 428.1304 \\ f18 & 583.18 & 577.8134 & 578.672 & 576.0805 & **573.5208** \\ f19 & 506.931 & 506.5545 & 506.6363 & 507.0156 & **506.3289** \\ f20 & 613.1458 & 613.154 & **613.113** & 613.594 & 613.423 \\ f21 & 1047.089 & 1051.01 & **1016.475** & 1035.483 & 1049.556 \\ f22 & **3871.804** & 3928.667 & 4109.614 & 4059.632 & 4032.769 \\ f23 & 5402.42 & 5574.529 & 5524.135 & 5597.751 & **5338.983** \\ f24 & 1264.25 & 1265.845 & 1265.61 & 1268.231 & **1264.214** \\ f25 & 1390.105 & **1387.764** & 1387.808 & 1390.035 & 1391.654 \\ f26 & **1408.752** & 1412.901 & 1424.752 & 1414.98 & 1412.238 \\ f27 & 2203.579 & 2187.724 & 2192.054 & 2191.372 & **2181.232** \\ f28 & 1812.154 & 1762.647 & **1707.262** & 1771.612 & 1830.575 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean value and average rankings achieved by AMdynFWA with different \(p\), where the ‘mean’ indicates the mean best fitness value. Figure 3 shows a comparison of the average run-time cost in the 28 functions for AFWA, EFWA, dynFWA, and AMdynFWA.