This work proposes a new efficient meta-heuristic optimization algorithm called Gamma Ray Interactions Based Optimization (GRIBO). The algorithm mimics different energy loss processes of a gamma-ray photon during its passage through a matter. The proposed novel algorithm has been applied to search for the global minima of 30 standard benchmark functions. The paper also considers solving real optimization problem in the field of nuclear engineering, radioisotope identification. The results are compared with those obtained by the Particle Swarm Optimization, Genetic Algorithm, Gravitational Search Algorithm and Grey Wolf Optimizer algorithms. The comparisons indicate that the GRIBO algorithm is able to provide very competitive results compared to other well-known meta-heuristics.

这项工作提出了一种新的高效元启发式优化算法，称为基于伽马射线交互的优化（GRIBO）。该算法模拟了伽马射线光子在穿过物质过程中的不同能量损失过程。所提出的新算法已被应用于搜索 30 个标准基准函数的全局最小值。论文还考虑解决核工程、放射性同位素识别领域的实际优化问题。将结果与通过粒子群优化、遗传算法、引力搜索算法和灰狼优化器算法获得的结果进行比较。比较表明，与其他著名的元启发式算法相比，GRIBO 算法能够提供非常有竞争力的结果。