dvanced Algorithm for Accurate Radiation Source Detection and Localization in Nuclear Emergencies by Robotic Systems

ccurate localization of radiation sources is a critical challenge in environmental monitoring, medical diagnostics, and nuclear safety due to the effects of environmental noise, computational inefficiencies, and limitations in sensor networks. This study aims to address these challenges by applying and comparing two metaheuristic optimization algorithms: particle swarm optimization (PSO) and biogeography-based optimization (BBO). The localization process is modeled as an optimization problem, with the objective of minimizing the Euclidean distance error between the estimated and true source positions. Simulated experiments were conducted under varying operational conditions, including changes in sensor area, radiation intensity, and signal-to-noise ratio (SNR). Numerical results reveal that BBO achieves a 25% lower root mean square error (RMSE) compared to PSO, with RMSE values decreasing from 0.58 at 5 dB SNR to 0.10 at 20 dB SNR. While PSO demonstrates faster computation times, as low as 1.2 10 seconds compared to 1.4 10 seconds for BBO, the latter provides superior localization accuracy, particularly in low-noise conditions. Additionally, contour plots validate the effectiveness of both methods, showing convergence of the estimated positions close to the true source locations. This work contributes to the field by providing a detailed comparative analysis of PSO and BBO for radiation source localization and identifying their respective advantages in terms of accuracy and efficiency. Future research could explore hybrid optimization techniques that combine the strengths of both algorithms and extend the study to three-dimensional localization and real-world noise conditions. These advancements would further enhance the applicability of optimization-based radiation localization in practical scenarios.