# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 5 Summary and Conclusions
### Conclusions  
This research article introduced a novel optimization technique called hybrid HHAOA, which combines two existing algorithms, HHO and AOA, to achieve a better performance. In order to assess the efficacy of the HHAOA algorithm, tests were conducted on a total of 33 optimization benchmark functions. The analysis involved a comparison of performance results, which were based on various measures, including mean, global best, worst, and standard deviation. The convergence performance of the HHAOA algorithm is faster at achieving global minima with fewer iterations than other algorithms. The comparison results were evaluated using Friedman ranking, which showed that the proposed HHAOA algorithm significantly outperforms various algorithms with a 181.342% increased performance ranking in terms of the final mean value. In addition, the best connectivity, network overlapping minimization, and optimal router placement for WMN using the proposed HHAOA were simulated for 500 iterations with 100 search agents. The HHAOA produced the most satisfactory desired performance by creating the best client router connectivity with only one client disconnected in the network. Additionally, the network overlapping was significantly reduced, with a 31.7% reduction in the WMN routers substantially minimizing the operational cost. Experimentation was conducted on a real-time pressure process to further demonstrate the HHAOA algorithm's effectiveness. The findings showed that the proposed algorithm performed best in the dynamic processes. Furthermore, using HHAOA-optimized FOPPI resulted in a more reliable, smooth, and robust control signal, leading to quicker settling and reduced peak overshoot which, in turn, significantly minimizes the wear and tear on the control valve. As part of future research, newer evolutionary algorithms with different mathematical operators will be investigated to enhance the algorithm's performance. Additionally, attempts will be made to hybridize the HHAOAalgorithm with other metaheuristic optimization algorithms to widen its applicability to more complex, real-time industrial and engineering problems.  
Conceptualization, R.I. and M.O.; methodology, P.A.M.D.; software, P.A.M.D.; validation, P.A.M.D., K.B. and H.A.; formal analysis, P.A.M.D.; investigation, P.A.M.D. and K.B.; resources, R.I. and M.O.; data curation, P.A.M.D. and H.A.; writing--original draft preparation, P.A.M.D. and K.B.; writing--review and editing, P.A.M.D. and K.B.; visualization, P.A.M.D. and H.A.; supervision, R.I. and M.O.; project administration, R.I. and M.O.; funding acquisition, R.I. and M.O. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Yayasan Universiti Teknologi PETRONAS-Prototype Research Grant 015 PBC-001.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors would like to acknowledge the support from Universiti Teknologi PETRONAS for providing the research facilities.  
The authors declare no conflict of interest.