## 2 Literature Survey
(2018) presented a balanced-energy-efficient-grid-based clustering protocol (BEEG). LEACH, k-medoids-LEACH (K-LEACH), distributed cluster head scheduling (DCHS), modified-LEACH and energy-LEACH (a clustering protocol based on a balanced network of (BEEG)) have been shown to improve network performance and longevity when compared to other protocols [18]. [PERSON] et al. (2015) developed an intra-sensor hybrid cluster protocol based on the distance betweenthe base station, node, and residual energy, using a combination of FCM and LEACH. As a result, the NLT is extended. It has been demonstrated that hybrid protocols are more efficient [19]. [PERSON] et al. (2017) proposed a multi-objective-optimization (MOO) technique and survey of recent R&D efforts. Several popular MOO techniques have been discussed, including heuristic/metaheuristic optimization algorithms and various other advanced optimization techniques [20]. [PERSON] et al. (2016) have proposed an improved PSO-based clustering-energy-optimization (EPSO-CEO) algorithm for WSNs. In this case, the PSO algorithm is used for cluster-head selection and clustering to reduce the amount of power consumed by WSN [21]. [PERSON] et al. (2015) have suggested a deployment strategy for WSNs which uses the artificial fish swarm algorithm (AFSA). To achieve an adequate QoS in WSNs, the sensor nodes must be deployed as optimally as possible [22]. [PERSON] et al. (2016) suggested the multiple-sink placement algorithm based on PSO, called PSO-MSPA. The algorithm is extensively tested in different WSN scenarios by changing the total number of gateways and SNs; and the algorithm's efficiency is demonstrated by analyzing the results [23]. [PERSON] et al. (2020) proposed that the optimal path in routing be used to enhance network lifetime and energy efficiency. Although several metaheuristics, especially PSO, have been widely used, they lead to poor-local-optimum problems [24]. [PERSON] et al. (2016) evaluated the effectiveness of various optimization techniques, including the hybrid GA-PSO approach, bacterial foraging optimization (BFO), GA, and PSO. First, optimization methods such as BFO, PSO, and GA are individually applied to WSN, and then GA and PSO are combined. Existing research integrates load balancing and GA optimization. However, other methods apart from mixing GA and PSO are considered here. This will also help to extend the network's life [25]. [PERSON] et al. (2017) described the swarm-intelligence method in a clustering approach with a consistent-clustering approach dependent on the artificial bee colony (ABC). The nodes' remaining energy, location, and density are all factors that reduce energy utilization. Greater network-topology flexibility is achieved through proper cluster-head selection, and node-power utilization is reduced by extending the network lifetime. The swarm-intelligence approach does not address the load-balancing issue [26]. [PERSON] et al. (2015) presented a tree-cluster that is based on a data-gathering algorithm (TCBDGA). A weight-dependent tree-building method was developed. As the network ages, TCBDGA maintains load balancing and reduces power consumption. Using TCBDGA lengthens the classification process [27]. [PERSON] et al. (2017) proposed a hybrid routing algorithm for heterogeneous networks. Weighted probabilities are used to assign cluster heads to network clusters. They consider combining active and passive networks to achieve well-organized data transfer and increase network lifespan. The energy optimization could have been carried out more efficiently [28]. [PERSON] et al. (2016) created battery-operated energy-harvesting WSN. The overhead issues were not addressed in BPSNs and EHSNs [29]. [PERSON] et al. (2015) proposed the genetic algorithm (GA), which provides a low-energy method for multipath routing. For scheduling, data were collected from multiple nodes. As a result, the optimum solution was discovered. Routing stability was not considered for efficient data transfer [30]. [PERSON] et al.