## 2 Literature Review
### Related Works  
The firefly contribution using Firefly Cyclic Randomization (FCR) for Cluster Head Selection (CHS) in WSN was proposed by [PERSON] et al. in 2022 [20]. Based on three distribution functions, the method finds the randomly generated solution. Additionally, by changing the random vectors of GWO, the study is based on the second method, Firefly Cyclic Grey Wolf Optimization (FCGWO). The study is carried out and described in terms of network longevity, energy efficiency, and live nodes for the three distributions. In 2021, [PERSON] et al. [21] suggested the hybrid optimization technique for CH selection. The preparation phase, transmission phase, and measuring phase are the three steps that make up the proposed CH selection. Initialization of the network's energy and node mobility occurs initially. Here, multi-objective constraints that take into account delay, energy, and distance are used to select the threshold and CH. Data transfer from CHs to BS starts after the CHs have been identified. The residual energy generated by the nodes is finally updated during the measurement phase. In 2019, [PERSON] et al. [22] designed a multi-objective system that depended on delay, traffic rate, energy, cluster density, and distance. Consequently, energy-aware routing was performed using the developed MOFPL. The designed Multi-Objective Fractional Particle Lion Algorithm (MOFPL) system found the best CH from various CH in WSN. The best routing paths were then set up as per deployed multi-objective function. In addition, capable CHS with superior EE was accomplished by the MOFPL method. In 2020, [PERSON] et al. [23] offered an improved system for CHS using Taylor Kernel Fuzzy C-Means (KFCM) customized from the KFCM method. The modeled system designated the CH via the acceptability factor, which was reviewed by energy, distance, and trust. In addition, the advantage of the KFCM system was established concerning higher confidence and higher Energy Efficiency (EE). In 2019, [PERSON] et al. [24] recognized a cluster formation method using the Firefly (FF) model and the Hierarchical Maximum Likelihood (HML) system in WSN to enhance EE and to lessen the cost-utility. The problems in the FF method were overcome by integrating the Hierarchical Maximum Likelihood HML hypothesis with it. Additionally, the allotment of power in SNs was completed accurately via the maximum likelihood property of HML. Latterly, the outcomes exposed the better cost function of EE using the adopted model. In 2019, [PERSON] et al. [25] employed a Mobile Energy Aware Cluster Based Multi-Hop (MEACBM) routing scheme for a variety of hierarchical WSNs. As per the novel probability procedure, the CHs were chosen as the finest; mainly, the SNs were elected as the CH, which offered superior energy over other SNs. This method considerably reduced the energy use of SNs to move the info to BS. The outcomes showed improvements over existing models regarding throughput, CH, life span, and dead nodes. In 2019, [PERSON] et al. [26] presented a clustering method that chose CHs via GWO. To elect CH, optimization was performed on the basis of leftover energy. In addition, to achieve better EE, the GWO system employed the cluster in a large number of consecutive rounds. This permitted the scheme to collect the energy that was required to improve CHS. Finally, the outcomes recognized that GWO assured a capable network life span. In 2020, [PERSON] et al. [27] deployed BOA to decide the best CH from diverse SNs. As a result, the Butterfly Optimization Algorithm (BOA) method aimed to decrease the use of energy and raise the life span of the system. The path amid BS and CH was represented using Ant Colony Optimization (ACO), and it elected supreme routes based on node degree, EE, and distance. Latterly, the advantage of BOA was established for nodes and energy use. In 2019, [PERSON] et al. [PERSON] et al. (2020) discuss the experience of implementing a suggested neural network-based routing protocol technique. Our method focuses on enhancing clustering performance.