## 2 Chimp Optimization Algorithm
### Mathematical Model: Chimp Optimization
### Computational Results and Analysis

To verify the performance of the comparison algorithm, this paper validates three flexible job shop problem instances consisting of 8 workpieces with four processes, each on eight machines for the 8 \(\times\) 4 \(\times\) 8 partially flexible job shop scheduling problem; 10 workpieces with four processes, each on 10 machines for the 10 \(\times\) 4 \(\times\) 10 fully flexible job shop scheduling problem; and 15 workpieces with four processes, each on 10 machines for the 15 \(\times\) 4 \(\times\) 10 fully flexible job shop scheduling problem. The Chimp optimization algorithm with a modified hybrid improvement strategy is compared with several example tests, and the FJSP is not only to solve the machine selection problem but also to solve the all-process sequencing problem. The computational results of the three objective values for the three flexible job shop example problems: the 8\(\times\)8 problem, 10 \(\times\) 10 problem, and 15 \(\times\) 10 problem, are given in Table 5. Also given is a comparison of the test results of the Chimp optimization algorithm using the hybrid improvement strategy in this paper with the KACEM [33] method and the XIA [34] method, in the case of different performance indicator functions, which include the minimum maximum completion time \(C_{M}\), the minimum maximum load machine load \(W_{M}\), and the minimum total workload \(W_{T}\) on all machines. As can be seen from Table 5, for the three test instances, the Chimp optimization algorithm with the improved hybrid improvement strategy achieved better test results on \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Example** \\ **Problem** \\ **(n \(\times\) m)** \\ \end{tabular} & \begin{tabular}{c} **Objective** \\ **Function** \\ \end{tabular} & \begin{tabular}{c} **Methods Such** \\ **as KACEM** \\ \end{tabular} & \begin{tabular}{c} **Methods Such** \\ **as XIA** \\ \end{tabular} & \begin{tabular}{c} **Best** \\ **\end{tabular} & \begin{tabular}{c} **Population** \\ **\end{tabular} & \
\begin{tabular}{c} **Average** \\ **Time \(\&\)s** \\ \end{tabular} &
\begin{tabular}{c} **Best** \\ **Time \(\&\)s** \\ \end{tabular} \\ \hline \multirow{3}{*}{\(8\times 8\)} & \(C_{M}\) & 15 & 16 & 15 & 16 & 14 & & 16.5 & 14 & \\  & \(W_{M}\) & N/A & N/A & 12 & 13 & 11 & 200 & 11.2 & 12 & 1.4 \\  & \(W_{T}\) & 79 & 75 & 75 & 73 & N/A & & N/A & 75 & \\ \hline \multirow{3}{*}{\(10\times 10\)} & \(C_{M}\) & 7 & 7 & 7 & 7 & & 14.5 & 7 & \\  & \(W_{M}\) & 5 & 6 & 5 & 200 & 9.0 & 5 & 3.2 \\ \cline{1-1}  & \(W_{T}\) & 45 & 44 & N/A & & N/A & 44 & \\ \hline \multirow{3}{*}{\(15\times 10\)} & \(C_{M}\) & 24 & 12 & & & 0 & 12 & \\  & \(W_{M}\) & 11 & 11 & N/A & N/A & 0 & 11 & 10.3 \\ \cline{1-1}  & \(W_{T}\) & 91 & 91 & & & 0.44 & 91 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The convergence curves of SDChoA. ## 6 Conclusions

To sum up, intra-shop task distribution in a manufacturing enterprise ensures that the right resources are efficiently assigned to perform work items/tasks with special consequences at the right time. This paper proposes an improved Chimp optimization algorithm based on adaptive position updating and dynamic weighting strategy enhancement and subsequently uses various standard benchmark functions to evaluate the optimization performance. Experimental results show that SDChOA has excellent performance in terms of convergence speed and accuracy compared with many population-based algorithms. The SDChOA search space has high accuracy even in complex environments. In addition, the flexible job shop scheduling problem under three performance objectives (minimum maximum completion time, minimum maximum machine load, and minimum total load of all machines) was studied using the same example test in the literature, and SDChOA was compared with other algorithms in the literature.