# A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization
## 4 Conclusions  
This study presents the development of a hybrid modified particle swarm optimization (PSO-SAO) algorithm, which integrates the trailing mode of the smell agent optimization (SAO) algorithm to enhance the velocity update process of the original PSO algorithm. This hybridization aims to address the challenge of suboptimal convergence encountered in the standard PSO algorithm. The performance of the proposed hybrid PSO-SAO was assessed using 37 distinct benchmark test functions. Evaluation metrics comprised the determination of the best, average, worst, and standard deviation values for the different algorithms, followed by a comparative analysis against the performance of both the original PSO and SAO algorithms. The experimental results obtained revealed that the PSO-SAO achieved the best results in 71% of the 37 functions, followed by the SAO with 62% and the standard PSO with 41%. These findings substantiate the efficacy of the developed PSO-SAO. The primary challenge of the hybrid mechanism is the introduction of the increased computational complexity, which can potentially impact the system efficiency and performance. This challenge necessitates further research to optimize and mitigate the computational overhead. Nevertheless, our hybrid algorithm enhances existing research in optimization by demonstrating the combination of functions from two different optimization algorithms, thus leveraging their individual strengths to achieve improved solutions. This approach led to a robust and efficient optimization technique, thus offering solutions that may be superior to those obtained using the individual methods. Furthermore, it is essential to emphasize that this research serves as a preliminary step towards the establishment of a robust optimization approach for hyperparameter tuning in supervised machine learning models and deep learning models, specifically designed for natural language processing and pathloss modeling. Future investigations will extend the use of the developed PSO-SAO to address various engineering applications, including hyperparameter optimization in deep learning models, language translation, path loss modeling, and route planning, among others. **Author Contributions:** conceptualization, A.T.S. ([PERSON]), H.B.-S. and A.T.S. ([PERSON]); methodology, H.B.-S., A.T.S. ([PERSON]) and A.J.O. ; writing--original draft preparation, H.B.-S. and A.T.S. ([PERSON]); writing--review and editing, H.B.-S., A.J.O. and A.D.A. ; supervision, M.B.M. and E.A.A. ; funding acquisition, H.B.-S. and A.J.O. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors would like to thank the Tertiary Education Trust Fund (TETFUND), Nigeria, for funding this research as part of the 2021 National Research Fund (NRF) grant cycle, under the project titled \"A Novel Artificial Intelligence of Things (AIoT) Based Assistive Smart Glass Tracking System for the Visually Impaired\" (TETF/DR&D/CE/NRF2021/SETI/ICT/00140/VOL-1). We would also like to thank the Image Processing and Computer Vision (IPCV) research group at Ahmadu Bello University Zaria, Nigeria, for their insightful comments and ideas that aided in the development of this study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ranking** & **Algorithms** & **Count of Best Values** & **Percentage (\%)** \\ \hline
1 & PSO-SAO & 28 & 76 \\
2 & SAO & 23 & 62 \\
3 & PSO & 15 & 41 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ranking of the different algorithms based on the number of functions for which they achieved optimal values. \begin{table}
\begin{tabular}{c c c} \hline \hline Function & F37 \\ \hline Metrics & Average & Std & Error \\ SAO & \(5.00\times 10^{-182}\) & \(2.71\times 10^{-133}\) & \(5\times 10^{-182}\) \\ PSO & \(5.36\times 10^{1}\) & \(1.35\times 10^{-5}\) & \(53.6\) \\ PSO-SAO & \(1.27\times 10^{-142}\) & \(5.35\times 10^{-182}\) & \(1.27\times 10^{-142}\) \\ \hline \hline \end{tabular}
\end{table}