## 2 Related Works  
[PERSON] et al. [16] considered the development of an Intelligent Aquila Optimizer Algorithm-Based NL Scheme (IAOAB-NLS) for WSNs. This introduced approach creates the application of ANs for detecting a suitable NL. Additionally, the IAOAB-NLS algorithm could be inspired by Aquila behavior. The IAOAB-NLS technique can have the capability of achieving suitable coordinated points of the nodes in the network. In [17], an efficient metaheuristic-based Group Teaching Optimizer Algorithm was developed for the NL (GTOA-NL) method for WSNs. To guarantee the effectual NL performance of this introduced GTOA-NL approach, a wide-ranging set of models was implemented to focus on the excellence of the GTOA-NL approach. [PERSON] et al. [18] developed a Modified Archimedes Optimizer Algorithm (MAOA) and DV-Hop termed as MAOADV-Hop. Primarily, PSO and tent chaotic mapping techniques have been presented in AOAs. Secondarily, the MAOA was exploited to exchange the least square (LS) part of the DV-Hop localized method.  
In [19], an NL method for WSNs that depends on virtual partition and distance correction (VPDC) was developed. Primarily, the distance of every hop on a shorter transmission path between the UN and beacon node is considered through the VP approach; next, the shorter communication path length was acquired by including every hop distance; at last, the distance among nodes was accomplished using the optimum path search method and the DC procedure. [PERSON] et al. [20] introduced a metaheuristic optimization-based NL and multi-hop routing protocol with MS (MONL-MRPMS) for WSNs. This method targets to attain energy efficiency with accurate NL effectiveness and includes an efficacious Coyote Optimizer Algorithm (COA) for NL (COA-NL) in WSNs. Furthermore, a seagull optimization-based multi-hop routing (SGO-MHR) model was developed for optimally choosing routes for inter-cluster transmission. In [21], an innovative iterative localization approach termed CVX-DV-Hop was developed in this communication. Firstly, it achieves matrix transformation for reformulating the original optimizer issue into one with nonconvex and convex constraints. Secondly, first-order Taylor expansions have been utilized to compress the nonconvex constraints into linear inequality limits. At last, a successive convex approximation technique was developed for iteratively solving the optimization difficulties.  
[PERSON] et al. [22] recommended a quantum annealing BA (QABA) approach for NL in WSNs. QABA integrates quantum development and an annealing approach to the model of the BA for enhancing global and local search capability and lastly, converging to better-optimized values. In [23], an opposition-based learning and parallel strategy AGTO (OPGTO) technique was developed. This study develops communication schemes among clusters for various kinds of optimizer issues. Eventually, the OPGTO technique is implemented for the 3D localization of WSNs in real time.  
An important research gap in the domain of WSN node localization is the pressing need for improving metaheuristics. However, considerable efforts are dedicated to emerging localization systems as the existing approaches frequently struggle to deliver the needed accuracy, energy efficacy, and scalability required by real-world WSN deployments. Enhanced metaheuristics are vital for addressing these problems by optimizing localization systems for handling noisy sensor data, adjusting to dynamic environmental conditions, minimizing energy consumption, and ensuring robustness in the face of node failures or adversarial attacks. Bridging this gap in metaheuristic research could advance the field of WSN node localization to enable the extensive and reliable execution of WSNs in various applications.