## 5 Experimentation Part Text-Mining
### Proposed Multi-Objective Evolutionary Algorithms
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & \multicolumn{2}{c}{**GA-SA (Instance 1 to 32)**} & \multicolumn{2}{c}{**Proposed HMFO**} \\  & **Jobs** & **Machines** & **Processing Image** & \multicolumn{2}{c}{**GA-MA (Instance 33 to 35)**} & \multicolumn{2}{c}{**University of Michigan**} \\  & & & & & & **Makespan** & **University of Michigan** & **University of Michigan** \\ \hline Instance 1 & 3 & 5 & [1, 10] & 41 & 138.1 & **30.8** & **26.3** \\ Instance 2 & 3 & 7 & [1, 10] & 54.1 & 205.4 & **43** & **190.5** \\ Instance 3 & 3 & 10 & [1, 10] & 61.2 & 229.1 & **49.7** & **204** \\ Instance 4 & 3 & 5 & [1, 50] & 190.3 & 708.7 & **171** & **617** \\ Instance 5 & 3 & 7 & [1, 50] & 252.8 & 960.6 & **226.7** & **834** \\ Instance 3 & 10 & [1, 50] & 33.8 & 1273.3 & 301.7 & **110** \\ Instance7 & 3 & 5 & [1, 100] & 375.4 & 1307.1 & **319.3** & **1134** \\ Instance8 & 3 & 7 & [1, 100] & 531.9 & 1895.4 & **516.7** & **1644.9** \\ Instance9 & 3 & 10 & [1, 100] & 729.1 & 2830.5 & **698** & **2467.5** \\ Instance10 & 5 & 5 & [1, 10] & 35 & 140.4 & **24** & **126.1** \\ Instance11 & 5 & 7 & [1, 10] & 46 & 186.3 & **30** & **169** \\ Instance12 & 5 & 10 & [1, 10] & 51.5 & 199.9 & **43.7** & **177** \\ Instance13 & 5 & 5 & [1, 50] & 165.5 & 671.5 & **149.8** & **583.9** \\ Instance14 & 5 & 7 & [1, 50] & 225.2 & 951.2 & **201.7** & **828** \\ Instance15 & 5 & 10 & [1, 50] & 317 & 1303.6 & **306.8** & **1139** \\ Instance16 & 5 & 5 & [1, 100] & 325.5 & 1253.2 & **311.7** & **1098** \\ Instance17 & 5 & 7 & [1, 100] & 436.9 & 1909 & **410** & **1663** \\ Instance18 & 5 & 10 & [1, 100] & 610.3 & 2587.5 & **598** & **2257** \\ Instance19 & 7 & 5 & [1, 100] & 28.7 & 110.9 & **19.6** & **96.7** \\ Instance20 & 7 & 7 & [1, 10] & 39.3 & 162.2 & **24** & **146** \\ Instance21 & 7 & 10 & [1, 10] & 56.5 & 241.6 & **49** & **218** \\ Instance22 & 7 & 5 & [1, 50] & 159.7 & 607 & **143** & **524** \\ Instance23 & 7 & 7 & [1, 50] & 220.8 & 919.1 & **206** & **846** \\ Instance24 & 7 & 10 & [1, 50] & 304.6 & 1310.5 & **265.002** & **1150.9** \\ Instance25 & 7 & 5 & [1, 100] & 351 & 1422.9 & **305.37** & **1287.9** \\ Instance26 & 7 & 7 & [1, 100] & 426.1 & 1978.3 & 370.7 & **1725** \\ Instance27 & 7 & 10 & [1, 100] & 625.9 & 2664.1 & **544.5** & **2319.7** \\ Instance28 & 10 & 10 & [1, 200] & 939.04 & 9873.2 & **816.96** & **8597.6** \\ Instance29 & 15 & 15 & [1, 200] & 1554.12 & 22,505.2 & 1352.08 & **19579.5** \\ Instance30 & 20 & 20 & [1, 200] & 4778.07 & 80.577.2 & **4156** & **700.102.16** \\ Instance31 & 20 & 20 & [1, 200] & 7753.04 & 1000.073.4 & **6749** & **87263.8** \\ Instance32 & 20 & 20 & [1, 200] & 15,062.5 & 19778.5 & **13,115** & **17297.5** \\ Instance33 & 18 & 15 & [1, 200] & 531 & 13,340.3 & **502** & **12986** \\ Instance34 & 18 & 15 & [1, 200] & 810 & 2036.32 & 739 & **1956** \\ Instance35 & 18 & 15 & [1, 200] & 680 & 2267.88 & **593** & **1837.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the experimental instances with makespan and energy consumption values. The processing time of various operations of jobs on several machines in different process plans, the energy consumption, and reliability of all the machines are known earlier. Table 6 provides the energy consumed per unit time by each machine and the reliability of the machine. To solve the considered problem, a newly established bio-inspired Moth Flame Optimization algorithm (HMFO) was adopted, and further, it was mapped according to problem nature. The operations were assigned to the machines in such a way that the considered objective functions are satisfied and an optimal sequence is obtained. The above-discussed approach is implemented for all formulated instances to find the robustness of the algorithm.