## 5 Discussion  
The performance of the proposed HEMOGWO algorithm was systematically evaluated through empirical studies on both a set of standard multi-objective test functions and a real-world industrial task from the SCOS software system. In terms of the two key performance metrics, GD and IGD, HEMOGWO outperformed SMOGWO, NSGA-III, MOEAD_DE, MOPSO, and the original MOGWO. It consistently ranked first in the Friedman test,  
Figure 4: Comparison of time consumption of different algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Algorithm** & **Mean Rank** & **Final Priority** & **N** & **df** & **Iman-Davenport F** & \(p\)**-Value** \\ \hline \multirow{5}{*}{GD} & HEMOGWO & 1.44 & 1 & & & & \\  & SMOGWO & 2.22 & 2 & & & & \\  & MOGWO & 3.33 & 4 & & & & \\  & MOEAD\_DE & 4.94 & 5 & & & 3.32 \(\times\) 10\({}^{1}\) & 3.21 \(\times\) 10\({}^{-13}\) \\  & MOPSO & 5.94 & 6 & & & & \\  & NSGA-III & 3.11 & 3 & & & & \\  & HEMOGWO & 1.44 & 1 & 9 & (5, 40) & & \\  & SMOGWO & 2.00 & 2 & & & & \\  & MOGWO & 3.44 & 4 & & & & \\  & MOEAD\_DE & 5.0000 & 5 & & & 5.11 \(\times\) 10\({}^{1}\) & 2.59 \(\times\) 10\({}^{-16}\) \\  & MOPSO & 6.0000 & 6 & & & & \\  & NSGA-III & 3.11 & 3 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: [PERSON] test results for GD and IGD metrics on the SCOS problems (significant level is 0.05).  
indicating statistically significant advantages in solution accuracy, distribution uniformity, and stability.  
Further analysis reveals that the Tent chaotic mapping strategy effectively improved the spatial coverage of the population during the initialization phase, laying a solid foundation for subsequent optimization. The Levy flight mechanism enhanced the algorithm's global exploration capability and its ability to escape local optima. Additionally, the integration of the differential evolution operator improved local search precision in the later stages, significantly enhancing the concentration and convergence speed of the solution set. These enhancements demonstrated strong adaptability in the SCOS task, meeting the dual industrial requirements of system stability and service availability.  
Although the proposed method outperforms other algorithms in most tasks, the integration of complex evolutionary mechanisms has led to a slight increase in per-unit runtime. Nevertheless, from the perspective of overall optimization performance and industrial applicability, this computational overhead remains within an acceptable range, confirming the effectiveness of the algorithm in addressing real-world complex optimization problems.  
However, although this study incorporated real-world parameters and extended them based on their distributions to improve the representativeness and controllability of the experimental data, certain limitations remain. The current dataset primarily focuses on static modeling at the level of service parameters and does not adequately reflect the dynamic behaviors present in real industrial service composition processes. Therefore, the experimental results partially demonstrate the algorithm's optimization performance under idealized conditions and require further validation in actual industrial workflows or dynamic service environments.