# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### Metaheuristic Algorithms-Based HO Decision Approaches  
In telecommunication, the design problem can be of large scale and complex that requires more computational power and time. These issues become severe and more challenging with the increase in different user requirements like data rate, speed, capacity etc. The complexity of these real-world challenges and requirements constrains the use of conventional methods. Metaheuristic algorithm highlights a process that is meant to discover a satisfactory solution to a complicated and difficult-to-solve optimization issue. For real-world problems with limited resources (e.g., processing power and time), it is crucial to find a near-to-optimal solution based on imperfect or partial knowledge. Metaheuristic algorithms can be classified into multiple categories: evolutionary, trajectory, and nature-inspired approaches. We added several papers in this subsection that use nature-inspired and evolutionary methods to address handover problems. Figure 9 illustrates the fundamental principle of utilizing Nature-Inspired and Evolutionary algorithms in optimizing handover procedures. Due to multiple user requirements and dynamic behaviors, HCP values cannot be fixed to perform handovers. In article [97], the authors proposed a PSO algorithm along with mobility load balancing to get optimized HCP values for dynamic behaviors and requirements by using RSRP, UE velocity and load information. The performance evaluation was done based on HOPP and HOF. They compared fixed HCP values, optimized TTT, optimized HOM, and proposed scheme that utilizes TTT, HOM and offset. The simulation results showed that the HOPP and HOF have greatly improved compared to fixed or partial HCP optimization schemes. Using other KPIs can further validate the effectiveness of the proposed approach. Similarly, [105] devised a hybrid technique that combined the principles of PSO and SFO for 5G HetNets. The proposed adaptive particle-based sailfish optimizer (APBSO) determines weights depending on input parameters such as SINR, E2E Delay, HOP, Jitter, and Packet Loss in order to select the optimal network from a list of potential networks. These weights are then supplied to a Deep Auto Encoder section, which optimizes these weight values to perform handover decisions. The proposed technique performs well in terms of delay, HOP, stay time, and throughput when compared to earlier studies. The authors in [118] presented the PSO and PSO passive  
Figure 9: Schematic Diagram of Metaheuristic-based Handovers in HetNets. optimization algorithms to decrease handovers by relocating UEs between Macro and Femto cells based on their speed and data requirements. When compared to the PSO optimization approach, the PSOC optimization technique reduces handovers by 20%. The basic purpose of handover optimization approaches is to provide users with seamless services during mobility such that an up-to-mark QoS is available to every user in the network. The authors in [111] proposed an integrated optimization approach based on grey wolf optimization (GWO) and mayfly optimization (MFO). They employed the characteristics of GWO for network initialization and MFO for handover decisions. For the initialization phase, they considered security level, battery level, RSRP and QoS as input parameters for handover triggering. The Simulation results showed on the basis of delay, call drop, energy consumption and throughput. The authors of [104] also developed a hybrid approach to manage the frequent handovers and power consumption issues in HetNets. They employed cuckoo search (CS) and genetic algorithm (GA) for handover optimizations. Resultantly, they achieved better results with proper resource utilization and mobility in terms of throughput, delay, number of HOs and HO Failure Probability. They used the location and velocity information of the UEs to get the effective solution population from the cuckoo search algorithm, and passed these population values to a GA to get an optimal handover triggering value.