A standard network selection and resource allocation mechanism in 5G heterogeneous networks using hybrid heuristic algorithm with multi-objective constraints

The integration of various Random Access Technologies (RATs) within 5G Heterogeneous Networks (HetNets) to satisfy the diverse communication needs of the Internet of Things (IoT) causes significant challenges in network topology selection and resource allocation. Traditional approaches do not handle network congestion and poor user experience which necessitates the development of more efficient and intelligent network management strategies. The main novelty of the research work is to combine the two intelligent optimization algorithms to address the complexities of resource management for enhancing system performance. The integrated optimization algorithm also aims to reduce the latency and communication costs while enhancing resource utilization within the network. The novel Hybrid Snow Leopard and Dark Forest Algorithm (HSL-DFA) combines the strengths of the Snow Leopard Optimization Algorithm (SLOA) and the Dark Forest Algorithm (DFA) to optimize network performance based on the multiple objectives including resource utilization, makespan, Quality of Service (QoS), energy consumption, communication cost, congestion control, and latency. The HSL-DFA algorithm integrates the SLOA and DFA to leverage their respective advantages in solving complex optimization problems. It worked based on a position-updating process using current and mean fitness values. Here, the SLOA is used for the position-updating process if the current fitness exceeds the mean fitness and DFA is used for the opposite scenario. This approach ensures higher convergence rates and optimal solutions for complex network optimization problems. By addressing multi-objective constraints, the algorithm significantly improves network performance and provides a promising solution for the management of a 5G network. Various metrics are utilized to confirm the effectiveness of the proposed model. The results showed that the throughput of the proposed model was 93 at the 200th node.