# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 5 Conclusions  
This study proposes an innovative modified JAYA algorithm to handle both numerical optimization problems and feature selection problems, addressing a real-world optimization challenge. The developed algorithm's efficiency was measured on 20 standard test functions, and the analysis shows that EJAYA's productivity significantly increases in balancing exploitation and exploration. This indicates that the convergence rate has been substantially enhanced. In addition, EJAYA was used to address feature selection as a discrete problem, and its efficacy was confirmed using multiple UCI-standardized datasets. EJAYA's results were compared to other methodologies, including WOASAT, ALO, GA,  
Figure 5: The time complexity of the different methods (time is per second).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Functions** & **SCA** & **DA** & **GOA** & **MFO** & **PSO** & **CSA** & **JAYA** & **EJAYA** \\ \hline F1 & 3 & 8 & 7 & 5 & 2 & 6 & 4 & 1 \\ F2 & 2 & 8 & 7 & 5 & 3 & 6 & 4 & 1 \\ F3 & 7 & 6 & 5 & 8 & 2 & 4 & 2 & 2 \\ F4 & 8 & 7 & 6 & 2 & 3 & 5 & 4 & 1 \\ F5 & 5 & 8 & 7 & 1 & 4 & 6 & 2 & 3 \\ F6 & 3.5 & 3.5 & 7 & 8 & 3.5 & 3.5 & 3.5 \\ F7 & 4 & 8 & 3 & 2 & 6 & 5 & 7 & 1 \\ F8 & 6 & 7 & 8 & 2 & 1 & 5 & 4 & 3 \\ F9 & 7 & 6 & 5 & 8 & 2 & 4 & 2 & 2 \\ F10 & 5 & 8 & 6 & 3 & 2 & 7 & 4 & 1 \\ F11 & 4 & 8 & 7 & 5 & 2 & 6 & 3 & 1 \\ F12 & 4 & 6 & 8 & 2 & 1 & 5 & 7 & 3 \\ F13 & 7 & 4 & 4 & 1 & 8 & 4 & 4 & 4 \\ F14 & 7 & 8 & 4 & 5 & 2 & 6 & 3 & 1 \\ F15 & 3 & 8 & 7 & 4 & 2 & 6 & 5 & 1 \\ F16 & 3 & 8 & 6 & 4 & 2 & 7 & 5 & 1 \\ F17 & 4 & 8 & 7 & 5 & 1.5 & 6 & 3 & 1.5 \\ F18 & 3 & 8 & 6 & 5 & 2 & 7 & 4 & 1 \\ F19 & 4 & 8 & 6 & 1 & 3 & 5 & 7 & 2 \\ F20 & 7 & 1.5 & 4 & 8 & 1.5 & 3 & 6 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The results of the Friedman test.  
PSO, etc. The results of the proposed method revealed that EJAYA's performance is highly competitive in terms of optimal solutions and classification accuracy. For future investigations, EJAYA can be applied to other applications, including image processing and artificial intelligence training, etc.  
Future research endeavors may explore the application of EJAYA in areas such as image processing, artificial intelligence training, and beyond, to assess its adaptability and performance in diverse optimization challenges. Additionally, investigating its integration with emerging technologies and exploring novel problem domains can lead to valuable insights into the algorithm's extended capabilities and its relevance in addressing complex real-world problems. To demonstrate the practical results of the proposed algorithm, it is also recommended to implement it on large-scale datasets. Moreover, it is also worthwhile to apply EJAYA to solve scheduling problems under uncertainties.  
All authors ([PERSON] and [PERSON]) contributed to the study's conception, design, material preparation, data collection, and the writing and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This article does not involve any studies with human or animal participants.  
The datasets analyzed during this study are available in the UCI Machine Learning Repository, [[http://archive.ics.uci.edu/ml](http://archive.ics.uci.edu/ml)]([http://archive.ics.uci.edu/ml](http://archive.ics.uci.edu/ml)).  
The authors declare no conflicts of interest.