## 2 Literature Review  
Many researchers have worked in this area and contended with the matter of localization firmly. Various optimization algorithms like the cuckoo search optimization [13], bat algorithm [14], and butterfly algorithm [15] are applied to deal with finding the co-ordinates of the target node. An efficient global optimization approach [16] was put forward using swarm intelligence to locate sensor nodes in WSNs. The main aim of this algorithm was to lessen the objective function by measuring the average square error of all surrounding anchor nodes. Another optimization technique named cuckoo search optimization [13] was employed to discover the undiscovered nodes deployed in WSNs with a quicker convergence time. Bio-inspired algorithms like particle swarm optimization (PSO) techniques and bacterial foraging algorithms (BFA) were used repeatedly to resolve the multidimensional environment localization difficulties [17]. Both of these strategies have the advantage of reducing the transmissions to the base station while maintaining the node's energy, but the findings show that the suggested solutions have a trade-off issue. The BFA approach takes longer to locate unknown nodes but it does so more accurately, whereas the PSO algorithm finds them rapidly to a greater extent. A unique hybrid algorithm was developed using BFA and PSO to improve the accuracy of sensor node positioning [18]. By improving location accuracy and shortening convergence time, this approach overcomes many of the limitations of BFA. The genetic algorithm [19] was developed to lower the overall expenses by taking into account very few anchor nodes required for every sensor inside the network to get located itself. Binary PSO established the concept of using RSSI to evaluate the distance between the target and anchor nodes and hence reduced the power utilization due to faster convergence along with increasing the lifetime of the network. The Bat algorithm was designed as a better way to deal with the problem of localization and improve location accuracy [14]. It works well and addresses both difficulties by improving the accuracy and decreasing the computational time. In comparison to PSO, the Flower Pollination optimization technique [20] was developed to minimize the localization errors as well as time of computation. The RangeFree Firefly Algorithm [21] was introduced to discover the nodes in a three-dimensional environment. The sensor nodes were segregated into three levels with anchor nodes at the top and undiscovered nodes at the middle and lower levels. The edge weights were used to determine the unknown node's co-ordinates. Using this method, a considerable count of nodes can be located. To detect sensor nodes more perfectly, an upgraded algorithm on the basis of an artificial bee colony (ABC) [22] was suggested. This strategy enhances localization accuracy without increasing computational time. To boost the convergence rate, another approach on the basis of chicken swarm optimization [23] was presented. In comparison to PSO and BPSO, the suggested technique's findings demonstrate high convergence. The Grey Wolf optimization algorithm [24], which is based on the characteristics of grey wolves, was presented to discover maximal nodes with reduced calculation time. The Butterfly optimization approach [15] was proposed to improve WSN performance with respect to localization errors and decreased computations. By combining the symbiotic organisms search (SOS) algorithm with multi-group communication and quantum behaviour approaches, a novel optimization technique named the Symbiotic Organism Search Algorithm with Multi-Group Quantum Behavior Communication (MQSOS) [25] was implemented, which is fast and convergent and may be applied to solve actual problems with several arguments.