## 2 Related Works  
In this section, the main related works in the area are analyzed to justify the need for the proposed work. [PERSON] and [PERSON] (2020) [15] presented a hybrid approach of Elephant Herding Optimization with a Cultural Algorithm for optimum CH selection (EHO-CA) to increase the lifespan. The advantages of the belief space provided by the cultural algorithm were used to define a separation operator that effectively created different local optimum solutions in the search space in this presented EHO-CA approach. Moreover, the addition of belief space assisted in providing the balance between an ideal exploitation and exploration process and better search abilities in the selection of optimal CH. The presented EHO-CA method improved the distinguishing qualities by combining separation and clan updating operators for optimal selection of CH to increase the network's lifespan. The limitation of this proposed method was that it required more processing time. For obtaining an extended network lifespan in sensor networks, [PERSON] et al. (2019) [16] proposed a combined Artificial Bee Colony (ABC) and Bacterial Foraging (BF) (ABC-BF) based clustering method. The benefits of Bacterial Foraging Optimization were integrated with this proposed ABC-BFA approach for enhancing the local search ability of the ABC algorithm to achieve maximal exploitation and exploration of the parameters evaluated for the selection of CH. The proposed ABC-BFA approach was assessed in simulations utilizing a percentage of living and dead nodes and throughput using various SNs in the network. However, the packet drop in the proposed technique was high. [PERSON] et al. (2019) [17] investigated clustering SN to improve WSN lifespan. The Lion pride optimizer algorithm reduced energy consumption by grouping SN into clusters. For picking the best nodes as CHs, this method used two criteria: battery power range and distance from the sink. The remaining nodes that were not CHs are then joined to the CH closest to them. Clusters were produced in this manner. After clusters were constructed, data routing was facilitated by a direct virtual backbone, which was based in the sink node and made up of CHs. However, from the results, it was identified that the proposed method has a high processing overhead and attained less throughput. Through the development of the Rider-Cat Swarm Optimization (RCSO) algorithm for beginning communication in SN, [PERSON] et al. (2021) [18] built a platform for data transfer in WSN. The proposed RCSO method, which incorporated the ROA in the CSO algorithm, was used to build the CHs of the WSN nodes, and the CHs aid communication with the ideal CH, determined using the fitness function. The network was first established with initial energy, and the node mobility was regulated using the mobility model. During the setup phase, the CHs for data transmission from the nodes to the BS were determined using the clustering algorithm, which was created by determining the best threshold and CHs using the proposed RCSO method. Following the selection of the CHs, data transfer from the CH to the BS commenced. However, this method did not consider the Quality of Service (QoS) metric. [PERSON] and [PERSON] (2020) [19] proposed a novel routing protocol based on Ant Colony Optimization (ACO) and HSA with optimum parameter selection. This research looked at how to send packets quicker without sacrificing data quality. The results demonstrated that the proposed strategy is more effective than other ways of increasing the network's energy efficiency. However, the limitation of this work was that it only allowed for a meta-heuristic approach, which may have restricted utility in recovering prior network routing parameters. [PERSON] et al. (2020) [20] investigated the topic of reducing WSN energy consumption and proposed an energy-efficient routing protocol relying on an enhanced ABC algorithm. This research employed the modified ABC method to optimize the fuzzy C-means clustering and pick the optimal CH.