# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 4 HMAO Algorithms
### Routing Phase
These may include further research studies on enhancing the performance of HMAO in predicting UAV movements. By addressing these limitations and exploring novel extensions, future research can contribute to more refined and adaptable FANET solutions that balance the computational demands with network performance. Furthermore, we may test the HMAO algorithm under practical conditions to verify its convergence and robustness. Conceptualization, J.A. and M.Y.A. ; methodology, J.A. and M.Y.A. ; validation, J.A. and M.Y.A. ; investigation, J.A. and M.Y.A. ; resources, J.A. ; writing--original draft preparation, J.A. ; writing--review and editing, M.Y.A. ; project administration, J.A. and M.Y.A. ; supervision, M.Y.A. ; funding acquisition, M.Y.A. All authors have read and agreed to the published version of the manuscript. This work received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data will be made available on request. The authors wish to thank the editor and anonymous referees for their helpful comments on improving the quality of this paper. The authors declare no conflicts of interest. ## References

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