## 4 Conclusions  
This paper addresses the challenges in developing algorithms that perform optimal connection management with proper resource utilization for uninterrupted mobility in vertical handoff. Therefore, in this paper, a unique hybrid optimization technique has been developed that is helpful to both the user and the network. A hybrid of CS and GA is developed to maximize the performance of heterogeneous wireless systems in terms of minimizing latency and handover failure probability and enhancing throughput. The effectiveness of the proposed algorithms is measured through simulations. The simulation results show that the delay has been reduced, while maximizing the throughput and minimizing the connection failure probability. Comparative performance analysis demonstrates a high quality of service and more precise use of the technology (i.e., use of the available resources). The proposed integrated hybrid scheme will definitely stimulate the handover process by minimizing unnecessary handover and service interruption time. The real-time simulation results attest to the efficiency of the proposed approach for a constant speed of 20 km/h by an improvement of 17%, 8%, and 28% in terms of average throughput with respect to cuckoo, genetic, RSS, and random methods. This also reduces handover delay by 50%, 25%, 62%, and 87% compared to the above methods for speeds of up to 40 km/h. With increasing speeds, the probability of handover failure decreases, and it decreases to 25%, 12.5%, 25%, and 20% for the above methods for a constant speed of 40 km/h. In addition, the number of handovers for the above methods is reduced by 20%, 50%, 25%, and 75%.  
In the future, we would like to examine the performance of the proposed approach with variation in the number of users and power consumption analysis of user equipment devices.  
Conceptualization: K.J., S.T. and A.G.; writing--original draft preparation: K.J., A.G. and C.O.S.; methodology: A.A. and T.C.M.; writing--review and editing: A.A. and K.J.; investigation: C.O.S., A.G. and S.T.; supervision: A.G. and S.T.; visualization; A.A. and K.J.; software: T.C.M. and S.T. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the University of Agricultural Sciences and Veterinary Medicine and Technical University of Cluj-Napoca, Romania.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
Figure 5: Number of handovers of the hybrid model.  
**Acknowledgments:** This paper was partially supported by UEFISCDI Romania and MCI through projects ALPHA, ADCATER, iPSUS, Inno4 Health, AICOM4 Health, SmarTravel, Mad@Work, DAFCC, PREVENTION, NGI-UAV-AGRO and by European Union's Horizon 2020 research and innovation program under grant agreements No. 883441 (STAMINA) and 883522 (S4 ALLCTIIES) and the University of Agricultural Sciences and Veterinary Medicine and Technical University of Cluj-Napoca, Romania for their support. National Research Development Projects to finance excellence (PFE)-14/2022-2024 granted by the Romanian Ministry of Research and Innovation.  
**Conflicts of Interest:** The authors declare no conflict of interest.