In several applications, sensor node localization is becoming not only essential but also crucial in the case of Wireless Sensor Networks (WSNs). In this context, finding all the target node coordinates with the aid of anchor nodes is the primary objective of the localization issue, which frequently yields erroneous position estimations. Analyzing inaccuracies in distance estimate and understanding the localization geometry are emerging to be crucial for devising effective strategies aimed at reducing localization errors. Furthermore, the research proposes an Extemporaneity Bat Optimization Technique (EBOT) based on Deuce Adaptation. We provide modifications that enhance the exploitation features of the BOT algorithm to provide effective WSN attainment or performance. The exploration function of the BOT algorithm is improved by presenting EBOT adaptation 1, which is an improved global search technique. Further, an enhanced local search method is employed in EBOT adaptation 2 to improve EBOT’s exploitation capabilities. Since there is no need for an extra device, the suggested EBOT adaptations 1 and 2 do not raise the cost of the WSN. A unique Polarity Metamorphosis Strategy (PMS) that reinforces the mutation and crossover operations improves the approach’s overall exploration capability and the population’s heterogeneity. As a result, the study recognizes the importance of considering the localization geometry and ranging errors while performing localization. To improve the localization geometry in the proposed method, the research selects the principal nodes during triangulation based on a simple assessment called the Mutable Ambit Premised Localization (MAPL) Algorithm. The necessity to improve accuracy and decrease localization errors in WSNs without raising expenses is what spurred this study. The work’s results thus assert that the recommended technique localizes more target nodes while attaining a lower mean localization error with accelerated convergence in contrast to other existing algorithms.