# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 7 Limitations and Future Work  
The JADESCA algorithm has demonstrated strong performance across various benchmark functions and engineering design problems; however, several limitations and potential challenges must be acknowledged. One challenge is its scalability to high-dimensional problems, where efficiency and computational cost may become significant concerns. Additionally, the algorithm relies on predefined control parameters, which may require manual tuning, reducing its adaptability in dynamic environments. While JADESCA includes adaptive mechanisms to balance exploration and exploitation, the risk of premature convergence or insufficient exploration in complex landscapes remains. The hybridization of JADE and SCA also increases the algorithm's complexity, potentially leading to higher computational costs compared to simpler methods. Future work could focus on addressing these limitations by exploring the integration of adaptive control parameter mechanisms or hybridizing JADESCA with machine learning techniques to dynamically adjust these parameters based on real-time performance. Furthermore, expanding its application to  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Optimizer** & **Best Score** & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** \\ \hline JADESCA & 2994.471 & 3.5 & 0.7 & 17 & 7.3 & 7.71532 & 3.350215 & 5.286654 \\ MFO & 2994.471 & 3.5 & 0.7 & 17 & 7.3 & 7.71532 & 3.350215 & 5.286654 \\ DOA & 2994.471 & 3.5 & 0.7 & 17 & 7.3 & 7.715321 & 3.350215 & 5.286654 \\ RIME & 2994.731 & 3.500311 & 0.7 & 17 & 7.3 & 7.721184 & 3.350242 & 5.286657 \\ COA & 2994.965 & 3.500451 & 0.700009 & 17.00029 & 7.30423 & 7.719595 & 3.350564 & 5.286661 \\ AVOA & 2995.032 & 3.50001 & 0.7 & 17 & 7.305603 & 7.37853 & 3.350227 & 5.286662 \\ ZOA & 2995.666 & 3.500284 & 0.7 & 17 & 7.305977 & 7.733636 & 3.350936 & 5.287355 \\ SCSO & 2996.844 & 3.500198 & 0.7 & 17 & 7.457542 & 7.740782 & 3.350943 & 5.286907 \\ SHIO & 2998.583 & 3.501653 & 0.7 & 17 & 7.317075 & 7.795592 & 3.354308 & 5.28745 \\ GWO & 3001.793 & 3.500026 & 0.7 & 17 & 7.564603 & 7.796651 & 3.360743 & 5.28742 \\ FVIM & 3004.641 & 3.50206 & 0.7 & 17 & 7.617562 & 7.762973 & 3.358744 & 5.291876 \\ HHO & 3008.729 & 3.516346 & 0.7 & 17 & 7.705688 & 7.872776 & 3.353213 & 5.286708 \\ WOA & 3015.221 & 3.5 & 0.7 & 17 & 7.3 & 7.77653 & 3.419526 & 5.288819 \\ OMA & 3016.568 & 3.520695 & 0.701083 & 17.00012 & 7.405428 & 7.9063 & 3.355592 & 5.290619 \\ SOA & 3017.402 & 3.503132 & 0.7 & 17.03941 & 7.3 & 8.022402 & 3.352044 & 5.298658 \\ TTHHO & 3018.703 & 3.517373 & 0.7 & 17 & 7.3 & 8.025153 & 3.350215 & 5.30324 \\ STOA & 3024.978 & 3.550997 & 0.7 & 17 & 7.82667 & 7.917368 & 3.352365 & 5.287983 \\ AO & 3048.003 & 3.546055 & 0.7 & 17 & 7.705698 & 8.123628 & 3.436698 & 5.286873 \\ ROA & 3058.109 & 3.531841 & 0.7 & 17 & 8.228035 & 8.071254 & 3.479612 & 5.28709 \\ Chimp & 3075.128 & 3.6 & 0.7 & 17 & 7.3 & 8.3 & 3.366746 & 5.324488 \\ SCA & 3076.29 & 3.6 & 0.7 & 17 & 7.3 & 8.3 & 3.392345 & 5.31606 \\ FLO & 3797.939 & 3.584428 & 0.716881 & 19.75319 & 7.620048 & 8.08554 & 3.348653 & 5.488105 \\ CPO & 4632.48 & 3.552042 & 0.705571 & 23.37616 & 8.098939 & 8.13028 & 3.779752 & 5.477325 \\ SSOA & 1017738 & 3.502899 & 0.709589 & 25.14204 & 8.030486 & 7.967745 & 3.395998 & 5.381849 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Optimization algorithms of the speed reducer design problem.  
more complex, multi-modal, and high-dimensional problems will provide deeper insights into its scalability and efficiency. Another interesting direction would be the extension of JADESCA to multi-objective optimization and real-time optimization frameworks, where the algorithm's ability to balance competing objectives or constraints could be further leveraged. Additionally, incorporating problem-specific knowledge or domain expertise into the optimization process may enhance the algorithm's performance in specialized fields such as aerospace, materials science, and bioengineering.