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

(2025) 2025:18 Zhu et al. J Wireless Com Network https://doi.org/10.1186/s13638-025-02441-4 EURASIP Journal on Wireless Communications and Networking Open Access RESEARCH A standard network selection and resource allocation mechanism in 5G heterogeneous networks using hybrid heuristic algorithm with multi‑objective constraints Ronghua Zhu1, Asha Aiyyappan2*, Jeya Ramya Varatharaj3 and Joselin Jeya Sheela John4 *Correspondence: 1 Zhujiang College, South China Agricultural University, Guangzhou 510900, China 2 Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Thandalam, Mevalurkuppam, Tamil Nadu 602105, India 3 Department of Electronics and Communication Engineering, Panimalar Engineering College, Varadharajapuram, Poonamalle, Chennai, Tamilnadu 600123, India 4 Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu 602105, India Abstract 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. Keywords: 5G heterogeneous networks, Network selection, Resource allocation, Quality of service, Hybrid Snow Leopard and Dark Forest Algorithm © The Author(s) 2025. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Zhu et al. J Wireless Com Network (2025) 2025:18 1 Introduction The HetNets are largely employed in the 5G wireless network to solve the issues involved in wireless transmission [1]. With the rapid improvement of the Mobile Terminals (MTs), the 5G mobile transmission model is developed to improve the communication ability as compared to the 4G mobile transmission device and it also enhances the Spectrum Efficiency (SE) of the 5G system [2]. The 5G network combines distinct innovations including Unmanned Aerial Vehicles (UAVs), Mobile Edge Computing (MEC), IoT, Machine-To-Machine (M2M) communications, vehicular networking, cloud computing, Cloud Radio Access Networks (CRANs), Device-to-Device (D2D) communications, and so on [3]. The HetNets perform a significant role in the future generation transmission networks named 5G networks. The homogeneous cellular macro Base Stations (BS) with low power BS and high transmission power are underlying components in the HetNet [4]. The low power stations vary from the femto cells and pico cells to the relay nodes are considered as little cells. The HetNets are utilized for Global System for Mobile (GSM) networks. The HetNets employed distinct small and macrocells in the upcoming years to manage the spectrum scarcity issue in conjunction with the enhanced capacity requirements [5]. The resource allocation mechanism is utilized in the two-tier HetNets to enhance the SE with the BSs and Microcell BS (MBS) that can distribute the identical spectrum attribute [6]. The 5G cellular networks have the merits of load balancing, better energy efficacy, low cost, deep coverage, and high capacity and are very complex innovations to the enhancement of wireless transmission networks [7]. Moreover, the conventional spectrum attributes and the 5G HetNets attributes contain the BS access point, RF antenna, and buffer [8]. The 5G HetNets resource improvement allocates and tunes attributes based on the distribution and the requirements of user services to attain a better connection among the candidate activity requirements. This process is used for wireless transmission to enhance the usage efficacy of numerous network attributes and to reduce the consumption expenses produced by heterogeneous networks [9]. To confirm the candidate activity’s QoS, it is significant to experiment with very reasonable and effective network selection approaches for the distinct business demands of the candidates [10]. The HetNet’s network selection is partitioned into two categories. The initial one is horizontal handover which is adopted by similar network categories, and the next one is vertical handover which is done on the distinct network categories [11]. The existing network selection approaches are categorized into four categories including game theory, machine learning, Mult (...truncated)