Open AccessStudy of structure optimization of carbon nanotubes using hybrid genetic algorithm based on clonal selection principle
Author(s) -
Bao Wen-Xing,
Zhu Chang-Chun,
Wanzhao Cui
Publication year - 2005
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.54.5281
Subject(s) - simulated annealing , meta optimization , computer science , conjugate gradient method , stability (learning theory) , global optimization , carbon nanotube , genetic algorithm , optimization problem , algorithm , optimization algorithm , molecular dynamics , mathematical optimization , materials science , mathematics , nanotechnology , physics , machine learning , quantum mechanics
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.