## 5 Conclusions  
Given that the scheduling of the task process is one of the most vital issues encountered by the cloud environment, this paper proposed a new algorithm called the HWACO algorithm that improves the basic functions of the ACO algorithm in order to efficiently search through the solution space and return the best optimal solution. This was achieved by using two main functions, which are the Construct_ANT solution function and Update_Pheromone function, respectively, that try to find the best and optimal solution, as was mentioned in Section 3. As mentioned earlier, the selection of the best solution is based on the values computed using the objective formula which combines both the makespan and cost parameters. Thus, the proposed algorithm outperformed other algorithms in terms of cost and makespan in such a way that the makespan and cost of the entire process were minimized, and, at the same time, the performance of the cloud computing environment was increased. To support this claim, an experimental simulation was conducted along with the statistical \(t\)-test, which suggest the fact that the HWACO was able to attain an average of 3.83%, 16.54%, 25.34%, 8.66% and 57.11% of improvement when compared to the QANA, MIT-BPSO, ACO, MIN-MIN, and FCFS in terms of makespan, respectively, and 12.15%, 18.88%, 23.63%, 27.05% and 32.94% of improvement when compared to QANA, MIT-BPSO, ACO, MIN-MIN, and FCFS, respectively, in terms of cost. Therefore, it can be concluded that the introduced algorithm optimizes the performance of the task scheduler in terms of makespan and cost. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cost** & **Iteration** & **HWACO** & **FCFS** & **Improvement** & \(t\)**-Test Value** & \(p\)**-Value** \\ \hline \multirow{6}{*}{Number of Task 200} & 10 & 70 & 102 & 31.37254902 & 4.3422 & \(5.056\times 10^{-4}\) \\ \cline{2-7}  & 20 & 63 & 99 & 36.36363636 & 3.3508 & \(7.058\times 10^{-5}\) \\ \cline{2-7}  & 30 & 58 & 84 & 30.95238095 & 3.2443 & \(9.956\times 10^{-4}\) \\ \cline{2-7}  & 40 & 52 & 80 & 35 & 2.9457 & \(8.947\times 10^{-5}\) \\ \cline{2-7}  & 50 & 49 & 78 & 37.17948718 & 4.2498 & \(3.305\times 10^{-5}\) \\ \hline \multirow{6}{*}{Number of Task 400} & 10 & 110 & 143 & 23.07692308 & 3.9375 & \(1.084\times 10^{-4}\) \\ \cline{2-7}  & 20 & 103 & 145 & 28.96551724 & 2.0948 & \(2.009\times 10^{-4}\) \\ \cline{2-7}  & 30 & 89 & 130 & 31.53846154 & 3.0358 & \(5.045\times 10^{-4}\) \\ \cline{2-7}  & 40 & 80 & 126 & 36.50793651 & 2.3957 & \(6.084\times 10^{-5}\) \\ \cline{2-7}  & 50 & 75 & 122 & 38.52459016 & 3.0358 & \(9.083\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: \(t\)-Test for cost between HWACO and FCFS. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cost** & **Iteration** & **HWACO** & **MIN-MIN** & **Improvement** & \(t\)**-Test Value** & \(p\)**-Value** \\ \hline \multirow{6}{*}{Number of Task 200} & 10 & 70 & 97 & 27.83505155 & 4.5654 & \(4.455\times 10^{-4}\) \\ \cline{2-7}  & 20 & 63 & 84 & 25 & 5.4213 & \(6.048\times 10^{-4}\) \\ \cline{2-7}  & 30 & 58 & 79 & 26.58227848 & 4.2324 & \(4.484\times 10^{-4}\) \\ \cline{2-7}  & 40 & 52 & 77 & 32.46753247 & 5.2342 & \(2.304\times 10^{-5}\) \\ \cline{2-7}  & 50 & 49 & 75 & 34.6666667 & 4.3297 & \(1.398\times 10^{-5}\) \\ \hline \multirow{6}{*}{Number of Task 400} & 10 & 110 & 138 & 20.28985507 & 3.2497 & \(9.038\times 10^{-3}\) \\ \cline{2-7}  & 20 & 103 & 122 & 15.57377049 & 2.2491 & \(5.038\times 10^{-3}\) \\ \cline{2-7}  & 30 & 89 & 120 & 25.8333333 & 2.3423 & \(3.395\times 10^{-3}\) \\ \cline{2-7}  & 40 & 80 & 115 & 30.43478261 & 3.0382 & \(7.458\times 10^{-4}\) \\ \cline{2-7}  & 50 & 75 & 110 & 31.81818182 & 4.3948 & \(9.344\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: \(t\)-Test for cost between HWACO and MIN-MIN. **Author Contributions:** Conceptualization, C.C. and P.K. ; methodology, C.C. ; software, C.C. ; validation, C.C. and P.K. ; formal analysis, V.K.P. ; investigation, B.A. ; resources, K.R. ; data curation, K.R.