Fuente: PubMed "swarm" Appl Radiat Isot. 2026 May 8;235:112690. doi: 10.1016/j.apradiso.2026.112690. Online ahead of print.ABSTRACTSolving nonlinear and multivariate optimization problems remains a persistent challenge in radiation physics and nuclear instrumentation. This study presents an enhanced version of the Gamma Ray Interactions Based Optimization algorithm, a physics-informed metaheuristic that embeds the fundamental processes of gamma-ray interactions-including photoelectric absorption, Compton scattering, and pair production-into its search dynamics. The improved algorithm was applied to two representative problems in gamma spectrometry: (i) extraction of Gaussian energy broadening parameters in sodium iodide (thallium-activated) scintillation detectors, and (ii) parameterization of pair production cross-sections in silicon. In the first case, the method accurately reproduced experimental full-width-at-half-maximum values and outperformed optimization algorithms such as the genetic algorithm, particle swarm optimization, gravitational search algorithm, and grey-wolf optimizer in convergence speed and stability. In the second case, it reduced the maximum relative error of the fitted cross-section to 1.24%, lower than competing algorithms exceeding 2%. These findings demonstrate both the accuracy and physical interpretability of the method. By coupling optimization steps with radiation-interaction mechanisms, the proposed approach exhibits robustness under noisy or data-sparse conditions and provides a versatile framework for detector modeling and nuclear data analysis, confirming its potential as a reliable physics-based optimization tool for advanced applications in nuclear science.PMID:42107285 | DOI:10.1016/j.apradiso.2026.112690