## 5 Results and Discussion
The user comfort index of the optimization process is shown in Figure 7. The optimized comfort index values regardless of the optimizer used are constantly better than the comfort index value of the non-optimized system shown by the dark blue line. This shows the optimization algorithms are able to optimize the user comfort index. However, among all the algorithms, the three variants of IW-ABC (exponential in orange, linear decreasing in green, and linear increasing in purple) show better performance where the lines are \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Average** & **Max** & **Min** & **Friedman Rank** & **Holm Post Hoc** \\ \hline \multirow{4}{*}{**Fitness**} & ABC & 0.9799 & 0.9975 & 0.9633 & 4.1771 & **0 \textless{} 0.0125** \\  & IW-ABC (exponential) & 0.9853 & 0.9999 & 0.9596 & 2.9687 & 0.04898 \textgreater{} 0.016667 \\  & IW-ABC (linear increasing) & 0.9863 & 0.9999 & 0.9608 & 2.6667 & 0.3017 \textgreater{} 0.05 \\  & IW-ABC (linear decreasing) & 0.9869 & 0.9999 & 0.9722 & 2.3333 & \\  & BA & 0.9838 & 0.9977 & 0.9684 & 2.8542 & 0.106583 \textgreater{} 0.025 \\ \hline \multirow{4}{*}{**Comfort Index**} & ABC & 0.9913 & 0.9966 & 0.9821 & 5.9583 & **0 \textless{} 0.01** \\  & IW-ABC (exponential) & 0.9960 & 0.9997 & 0.9883 & 2.5521 & 0.518387 \textgreater{} 0.025 \\  & IW-ABC (linear increasing) & 0.9963 & 0.9997 & 0.9917 & 2.2292 & \\  & IW-ABC (linear decreasing) & 0.9962 & 0.9997 & 0.9910 & 2.2396 & 0.983379 \textgreater{} 0.05 \\  & FA & 0.9915 & 0.9961 & 0.9752 & 5.7083 & **0 \textless{} 0.0125** \\  & ACO & 0.9909 & 0.9975 & 0.9796 & 6.5000 & **0 \textless{} 0.008333** \\  & GA & 0.9901 & 0.9978 & 0.9793 & 6.8542 & **0 \textless{} 0.007143** \\  & BA & 0.9945 & 0.9994 & 0.9839 & 3.9583 & **0.000544 \textless{} 0.016667** \\ \hline \multirow{4}{*}{**Energy Consumption**} & ABC & 147.6226 & 214.8538 & 68.1349 & 4.1458 & **0 \textless{} 0.0125** \\  & IW-ABC (exponential) & 120.9565 & 222.4773 & 31.0000 & 2.8229 & 0.258636 \textgreater{} 0.025 \\ \cline{1-1}  & IW-ABC (linear increasing) & 116.1255 & 202.4568 & 31.0000 & 2.5312 & 0.821261 \textgreater{} 0.05 \\ \cline{1-1}  & IW-ABC (linear decreasing) & 117.0123 & 214.2358 & 27.6068 & 2.4583 & \\ \cline{1-1}  & BA & 131.1864 & 216.9695 & 68.0068 & 3.0417 & 0.070701 \textgreater{} 0.016667 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Algorithms Performance. closer to 1. The BA (apricot) and FA (sky-blue) are close to the proposed variants of IW-ABC. Meanwhile, the ABC (red), ACO (maroon), and GA (blue) perform worse but are, nonetheless, better than without optimization. The fluctuation of the comfort index is influenced by the fluctuation in the environment. Figure 6: Fitness vs. Time Instance. Figure 7: Comfort Index vs. Time Instance. The total power consumption of the optimization process can be seen in Figure 8. Similar to the fitness and comfort index, the energy consumption using the optimized parameters is better than the non-optimized system in the dark blue line. The optimization algorithms successfully reduce the energy usages. The exponential IW-ABC (green), linear increasing IW-ABC (orange), and linear decreasing IW-ABC (purple) are lower than ABC (red) and BA (sky-blue) for most of the time instance.