# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### Metaheuristic Algorithms-Based HO Decision Approaches
Instead of using a hybrid approach the authors in [112] used the basic GA for handover optimization process on the basis of data rate, bit error rate, RSRP and delay for HetNets scenario. The development of dense networks and HetNets has greatly increased the functional complexity by many folds. This rise in functional and optimization complexity directly affected the network performance in the form of KPIs degradation. Coverage and handover-related parameters are mainly affected in HetNets, which needs to be optimized in an efficient way such that the KPIs and QoE can be maximized. Heuristic algorithms and ML techniques allow you to model and map out functions in the form of data that cannot be evaluated directly or mathematically. Instead of using simple heuristic approaches, now, most authors prefer to use these approaches with ML or DL techniques [110]. Similarly, in [120], the author's used ML techniques with GA to get the optimal values for HOM and CIO that can resultantly enhance the SINR for users in the network. Firstly, they generated the synthetic data values for possible combinations of HOM and CIO. These generated values are forwarded to the ML model to predict the most optimal KPIs that can enhance the system's performance. Lastly, they used GA on the outputs of ML model for faster convergence. The performance analysis result shows that CatBoost performs best amongst ML techniques and GA can find the optimal convergence point more efficiently. To obtain optimal values for HCPs, the authors [107] used a data-driven multi-objective optimization approach. Firstly, they generated synthetic data for XGBoost, an ML model, to generate reliable KPI values, and then they used GA to maximize the RSRP and HOSR jointly. Later this work was extended by the same authors in [106]. The authors proposed a synthetic data-driven self-optimization approach for inter-frequency handovers by employing XGBoost and modified GA. They asserted that this is the first method of its kind that optimizes handover parameters with severely time-bounded constraints using synthetic data rather than real-time operator data. TTT, threshold1, and threshold2 are the three handover parameters that they optimized. The performance of these parameters was justified based on the handover success rate, RSRP, and SINR. The multi-Objective Optimization function determined the optimal values of TTT, Th1, and Th2. This designed objective function jointly maximizes the three KPI indicators: HOSR, RSRP, and SINR, and also maintains the fairness among KPI defined by the operators. This Optimization process was based on the training of Synthetic data designed with the help of SHAP implemented in the newly developed Simulator \"SyntheticNet\" [160] and produced with the help of the XGBoost gradient model. In the end, they used a Modified GA named Intelligent Mutation GA (IMGA) on the output values of XGBoost model for faster convergence to get optimal values. List of some of these studies have shown in Table 8 by considering different metahueristic algorithms. The development of metaheuristics and nature-inspired algorithms has proven to be a crucial method in optimizing handover procedures in HetNets. A multitude of studies have shown the effectiveness of such algorithms in reducing handovers and improving KPI's such as HOPP, HOF, delay, call drops, energy consumption, and throughput. The integration of machine learning and deep learning techniques has further enhanced the performance of these optimization algorithms.