搜索

x
中国物理学会期刊

基于克隆选择的混合遗传算法在碳纳米管结构优化中的研究

CSTR: 32037.14.aps.54.5281

Study of structure optimization of carbon nanotubes using hybrid genetic algorithm based on clonal selection principle

CSTR: 32037.14.aps.54.5281
PDF
导出引用
  • 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.

     

    Focusing on the problem of carbon nanotube structure optimization by molecule dynamics simulation, a novel algorithm is proposed which combines the genetic algorithm with simulated annealing and the clone select algorithm.Test results of five typical functions show that this algorithm has high stability and gives good global optimization. Applied to structure optimization of carbon nanotubes, it can accelerate the process of energy optimization and improve the quality of structure optimization.The simulation results show that the optimizing time increases linearly with the number of atoms.The time of structure optimization is reduced one order of maguitucle compared with the conjugate gradient methods.

     

    目录

    /

    返回文章
    返回
    Baidu
    map