Physical activity can be monitored via small low-power sensor nodes (SNs) that are widely dispersed over the earth. For WSN sensor nodes, GPS is one of the most commonly utilised localization algorithms. Military, industrial, and more recently, consumer and civil uses of GPS are all examples of its vast range of applications. Wi-Fi enabled smart sensors are the product of a combination of WSNs and embedded intelligent sensor structures. Building smart sensor systems relies heavily on AI methods. An innovative Hybrid DA-FA and several meta-heuristics are compared in this research paper as initial contribution. A single anchor node meta-heuristic algorithm is suggested to determine the location of a node using a range-based approach. In contrast to the randomly moving target nodes, the anchor node is fixed in the middle of the region. Line-of-Sight difficulties can now be alleviated to a greater extent thanks to the introduction of virtual anchor nodes. They have shown a significant improvement in localization accuracy and rapid convergence in mobility-based scenarios with a reduced number of anchor nodes. A comparison of the accuracy, localization error, and other metrics of both methods is included in the new approach. We have evaluated the DA-FA techniques performance for maximum error which is reduced to 21.53% in comparison of existing approach. However, the minimum error is reduced to 3.91%.