2026-10-14T20:03:33Z https://www.scidb.cn/oai

10.57760/sciencedb.hjs.00618 2026-02-11T13:40:39Z

2026-02-11 Research on radioactive waste disposal optimization algorithm based on biased random-key genetic algorithm doi:10.57760/sciencedb.hjs.00618 en Abstract [Background] The resources for radioactive waste disposal are extremely valuable, and their efficient utilization represents a core operational challenge for current disposal facilities. [Purpose] This study aims to address this challenge by introducing a Biased Random Key Genetic Algorithm (BRKGA) to develop an intelligent optimization algorithm specifically designed for the layout planning of radioactive waste packages. [Methods] The proposed algorithm incorporates four key innovative strategies. First, a three-segment gene encoding scheme was designed to represent the placement sequence, orientation, and radionuclide activity information of the waste packages. Second, a multi-objective fitness function was constructed that balances spatial utilization efficiency with radiation protection optimization; this was coupled with the introduction of a "Decreasing Sequence Priority Principle" to enhance the spatial allocation strategy. Third, algorithm acceleration was achieved through parallel computing implementation. [Results] Testing with an engineering case study demonstrated that the proposed algorithm significantly improves spatial utilization by over 6 % compared to manual design approaches, achieving a maximum utilization rate of 92.45 %. The algorithm exhibits excellent convergence characteristics, consistently obtaining high-quality solutions within an average of 342 generations. Furthermore, in a 32-core parallel computing environment, a speedup ratio of 10.34× was attained, reducing the computation time from several hours to just a few minutes. [Conclusions] The intelligent layout algorithm and the corresponding software system (Radioactive Waste Package Stacking system, RWPS) hold a significant value for practical engineering applications. Radioactive waste disposal; Biased Random-Key Genetic Algorithm (BRKGA); Optimization algorithms; Space utilization; Parallel computing 刘兴伟 吕涛 夏加国 陈杏子 李昶 李星宇 王馨 PUBLIC https://creativecommons.org/publicdomain/zero/1.0/ dataset Science Data Bank