## 2 Background and Related Work
Different kinds of EAs, including FA, PSO, and BOA, etc., have been successfully utilized during the last few years in solving different localization problems [29]. Since the performance of a wireless sensor network is significantly impacted by the accuracy of the sensor node localization, [PERSON] and [PERSON] [29] proposed the BOA method, a nature-inspired optimization algorithm, to reduce localization errors when calculating the position of sensor nodes in WSNs. In comparison to the PSO and FA based node localization systems, the proposed method demonstrates more consistent and precise node positioning results. Researchers suggested using PSO and TDOA combined, which significantly improves positioning accuracy [60]. Comparisons with established methods like WLS and LLS demonstrate that PSO is superior. In addition, the ML estimator is utilised to create the objective function for the hybrid TDOA/AOA location estimation issue, and the PSO improved with chaos is offered as a solution [15]. Comparative investigation demonstrates that the upgraded PSO algorithm outperforms the traditional optimization algorithms in terms of global search capability. To improve localization accuracy in WSNs while using fewer power and resources, researchers presented a two-stage PSO algorithm for locating wireless sensor nodes in concave space [61]. The authors proposed employing a TDOA passive positioning sensor selection approach based on the tabu search to balance the positioning accuracy of the sensor network and system utilization [62]. Consideration is given to the passive TDOA model, with sensor position errors taken into consideration, and the tabu search method is used to solve the transformed sensor selection problem. Simulation findings demonstrate that the performance of the suggested sensor optimization approach closely resembles that of the exhaustive search. A genetic algorithm with the flexibility to be modified for varied circumstances and ground models is proposed to select the appropriate node localization in order to decrease the CRLB in each sensor of an asynchronous TDOA architecture [63]. The proposed methodology allows for the optimization of a passive A-TDOA architecture's 3D sensor deployment, including ground modeling flexibility and the reduction of spatial discretization to achieve better results, which is critical for precision applications and indoor and outdoor navigation of automatic vehicles such as automatic ground vehicles and unmanned aerial vehicles. It has been established that combining two or more EAs can improve solution quality and boost optimization performance. Combining the WLS and FA algorithms, the Hybrid Firefly Algorithm (hybrid-FA) is proposed for estimating target coordinates from TDOA data [27]. According to the simulation findings, the hybrid-FA approach provides better performance and localization accuracy than the state-of-the-art localization approaches. Furthermore, the improved DE algorithm has been successfully hybridised with the FA method and deployed to address the passive target localization problem utilising TOA measurements [16]. The results reveal that the proposed method outperforms the standard algorithms in terms of localization performance. GA is hybridized with the DE algorithm in order to improve the performance in the WSN localization problem [64]. In addition, combining the DE algorithm with the standard direct search Nelder-Mead approach yielded higher quality solutions for an objective function defined using the ML method [65]. Moreover, a number of hybrid EA variants, such as DE and FA (HFDLA) [66], HPSOVNS [67], and PSO and variable neighbourhood search (HPSOVNS), are used successfully to improve positioning accuracy. The techniques for passive localization have found a wide range of applications in recent years due to their ability to accurately locate a target without relying on active sensors or emitting radio signals.