## 2 Related Works  
[PERSON] and [PERSON]. (2021) [9] developed the Artificial Bee Colony (ABC) Optimization Algorithm for enhancing routing in wireless networks. The Artificial Bee Colony (ABC) Optimization aimed at improving the performance and efficiency of wireless communication systems by applying nature-inspired optimization techniques. Comparative analysis of network performance was performed using performance metrics such as packet delivery ratio, latency, and energy efficiency between the ABC algorithm and existing models. The study underscores the significance of nature-inspired optimization algorithms, particularly the ABC algorithm, in improving routing efficiency, reducing interference, and enhancing overall network performance in wireless communication systems. It stated potential applications of the developed algorithm in addressing complex challenges and optimizing wireless networks for various technological advancements. Although the study enhanced the performance of the entire network, the proposed scheme was unable to select optimal paths based on dynamic network conditions and traffic patterns. [PERSON] et al. (2021) [10] also proposed an algorithm for energy-efficient clustering in heterogeneous wireless sensor networks. The algorithm was developed to improve energy efficiency in terms of intra-cluster communication by considering distance and energy factors. Their study aimed at reducing energy consumption within clusters by minimizing communication distances between nodes and cluster heads. The key components of the proposed algorithm include cluster formation, cluster head selection based on proximity and energy levels, and optimizing intra-cluster communication to reduce energy consumption. They presented simulation results, comparing the performance of the proposed protocol with the PEGASIS protocol in terms of PDR, average end-to-end delay, and energy consumption. The proposed algorithm outperformed the PEGASIS protocol in terms of lifetime, energy efficiency, and average end-to-end delay, thereby improving the lifetime of the wireless sensor network. Also, using proximity to the center of the zone and maximum residual energy as criteria for CH selection may affect the overall performance of the network since a faulty sensor may have more energy. This may disrupt effective data collection from some segments of the field. The proposed algorithm did not consider mobile sensors within the network, hence raising the scalability issue. Another study by [PERSON] et al. (2021) [8] developed a clustering algorithm based on a nature-inspired approach for energy optimization in a heterogeneous wireless sensor network. Their approach focused on the heterogeneity of devices, energy requirements of nodes, and network characteristics. The performance of the proposed scheme was measured based on the number of data packets successfully received at the base station; data transmission and reception were also efficient in the network. In [8], performance was compared with existing models such as Distributed Energy-Efficient Clustering (DEEC),Developed Distributed Energy-Efficient Clustering (DDEEC), and Extended version of Distributed Energy-Efficient Clustering (EDEEC). [PERSON] and [PERSON]. (2021) [9] proposed the Artificial Bee Colony (ABC) Optimization Algorithm as a method for enhancing routing in wireless networks. This algorithm is inspired by the foraging behavior of honeybees and is designed to optimize various performance metrics in wireless communication systems. The study aimed to provide a novel approach to routing that enhances efficiency and performance through the use of bio-inspired algorithms. Their algorithm initializes a population of artificial bees, which represent potential solutions (routing paths) in the network. Each bee is assigned a specific route to explore based on the network model. The behavior of the bees was simulated to mimic the natural foraging process.