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针对分子动力学模拟中碳纳米管的结构优化问题,提出了一种新的优化算法.新的优化算法在遗传算法的基础上,引入了克隆选择机理和模拟退火技术.对五个典型函数的优化测试结果表明,该算法搜索过程稳定性好,可较好地实现全局最优.将其应用于碳纳米管原子结构优化,加快了能量优化速度,提高了优化质量.模拟结果说明,混合遗传算法的优化时间随原子数增加而呈线性增长.在碳纳米管原子数较多时,结构优化时间比共轭梯度法降低一个数量级左右,大大降低了系统的模拟时间.
Aiming at the structural optimization of carbon nanotubes in molecular dynamics simulation, a new optimization algorithm is proposed.On the basis of genetic algorithm, the new optimization algorithm introduces the clonal selection mechanism and the simulated annealing technique.On the basis of five typical functions The results of optimization test show that the proposed algorithm has good stability and good global optimization, which is applied to optimize the atomic structure of carbon nanotubes, speed up the energy optimization and improve the quality of optimization.The simulation results show that the hybrid genetic When the number of carbon nanotubes is larger, the optimization time of the proposed algorithm decreases by about one order of magnitude compared with the conjugate gradient method, which greatly reduces the simulation time of the system.