## 2 Related Works  
In the literature, proposals for solving the location determination problem in WSNs typically involve metaheuristic algorithms [12]. In [13], these proposals are categorized into two main types: range-based and range-free, based on the distance information between nodes. In [14], a two-stage distance-based metaheuristic method was proposed for the positioning problem. In [15], a semi-definite programming technique is utilized for range-based localization. In [16], a metaheuristic method and machine learning algorithms were used together for optimal coverage of the sensors that constitute the WSN. The authors of [17] present an algorithm for range-independent localization based on multidimensional scaling. In the study, the distances between neighboring nodes are estimated for specific anchor node locations. PSO, including its classical model and various versions, has been utilized in numerous studies to determine node locations in WSNs. While the modified Particle Swarm Optimization (PSO) method was employed in [18], a two-stage PSO algorithm with enhanced sensitivity was utilized in [13]. In [19], a comparative study was conducted using two different techniques: the Radial Basis Function (RBF) neural network and the Multi-Layer Perceptron (MLP) neural network, to determine the locations of nodes in a 3D WSN. The results indicated that RBF offers greater localization accuracy, while MLP is more efficient concerning computational and storage resources.  
In [20], a positioning algorithm is proposed for a Mobile Node in a 3D WSN, which does not have the singularity problem in positioning; [21] presents a range-free positioning algorithm to address the large positioning errors encountered in 3D positioning algorithms in real field conditions. In [22], a localization algorithm was proposed that features low communication loss and a straightforward design, addressing the issue of nodes with unknown initial locations in a 3D WSN. In [23], a method of estimating distances using weighted least squares based on mobile beacons was proposed. In the 3D localization algorithm presented in [24], a method utilizing coplanarity units is implemented to enhance positioning accuracy.  
While the static anchor-based localization method is utilized in some 3D scenarios for WSNs, most research focuses on using the Mobile Anchor Node method to reduce the requirement for many anchor nodes [25,26]. In a study using a fixed anchor node [27], virtual anchors placed at 60-degree angles were used to assist in positioning. In the study, the detection area is limited by the positions of the virtual anchors. For the same positioning strategy, [28] introduces a modified learning enthusiasm-based teaching learning-based optimization (mLebTLBO), while [29] introduces the Adaptive Plant Propagation Algorithm (APPA) method. In [30], a single anchor node moving according to the Gauss-Markov three-dimensional mobility model was used for the positioning problem in a 3D WSN. The researchers used the mathematical formulation they developed to estimate the positions of unknown nodes. In [31], an algorithm that can produce more efficient results with lower cost was investigated. In their studies using mobile anchors, the researchers focused on the bounding box approach.