## 5 Results and Discussion  
The algorithms' average, maximum, and minimum of fitness, comfort index, and energy consumption from the 30 runs are tabulated together with the [PERSON] rank and the Holm post hoc procedure \(p\)-values in Table 4. The best of the average, maximum, minimum, and Friedman rank are highlighted in grey, while the Holm's \(p\)-values that indicate significant differences are in bold. The results of the fitness optimization, which combines both the comfort index maximization and energy consumption minimization, show that the IW-ABC with linearly decreasing inertia in the range between 0.9 and 0.4 is better than the others. The [PERSON] rank of this variant of IW-ABC is the best at 2.3333, while the Friedman statistic value is 37.695833. This shows significant differences exist between the algorithms. The Holm post hoc test shows that the linearly decreasing IW-ABC is significantly better than the original ABC, but is on par with BA. All variants of the IW-ABC investigated here show on-par performance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Runtime & 30 \\ Maximum iteration, \(t_{max}\) & 100 \\ Population size & 50 \\ Onlooker & Np/2 \\ Limit & (Np/2)*D \\ Fitness function ratio & \(a_{1}:a_{2}\) & 0.5:0.5 \\ \(p_{1}\) & 1/3 \\ \(p_{2}\) & 1/3 \\ \(p_{3}\) & 1/3 \\ \(P_{T}\) & 5 \\ \(P_{I}\) & 1 \\ \(P_{A}\) & 1 \\ Increasing inertia weight & \(\begin{bmatrix}\omega_{init}\,\ \omega_{final}\end{bmatrix}\) & \([0.6,1.0]\) \\ Decreasing inertia weight & \(\begin{bmatrix}\omega_{init}\,\ \omega_{final}\end{bmatrix}\) & \([0.9,0.4]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithms Parameters. The effect of the optimization algorithms toward each of the objectives is then studied using the optimized parameters. The dataset comes with results from the comfort index maximization using FA, ACO, GA, and BA, where the results are optimized without taking the energy consumption into consideration. Hence, for fairness, only the comfort index is statistically analyzed against these algorithms. The IW-ABC with linearly increasing inertia weight from 0.6 to 1 reported the best comfort index optimization performance. The comfort index found by the linearly increasing IW-ABC has the best Friedman rank followed by linear decreasing and exponential inertia weight. The comfort index found by BA and FA are better than the original ABC. However, the original ABC is better than ACO and GA in optimizing the comfort index. The Friedman statistic of 219.862847 showed a significant difference between the algorithms. The \(p\)-values of the Holm post hoc test indicate that the linearly increasing IW-ABC is significantly better than the original ABC, FA, ACO, GA, and BA, and has an on-par performance with the other two variants of the IW-ABC investigated here. In terms of the energy consumption minimization, the linear decreasing IW-ABC has the best Friedman rank. The [PERSON] statistic is 35.695833, rejecting the null hypothesis of on-par performance for all algorithms tested. The Holm post hoc \(p\)-values indicate the linear decreasing IW-ABC is significantly better than the original ABC but not significantly better than the BA. All the variants of the IW-ABC have on-par performance. The fitness of the optimization algorithms over the 48 time instances are illustrated in Figure 6. The optimization algorithms are able to improve the fitness value. As shown in the graph, after optimization using exponential the IW-ABC (green), linear increasing IW-ABC (orange), linear decreasing IW-ABC (purple), ABC (red), and BA (sky-blue), all the fitness values are above the dark blue line, which is the fitness value before optimization. The IW-ABC with linearly decreasing inertia weight provides the highest fitness value in comparison to the other algorithms in most of the time instances as can be seen from the purple line in the figure.