摘要
针对粒子群算法收敛速度不佳和易陷入局部最优的问题,提出了一种遗传量子粒子群优化(GQPSO)的属性约简算法,GQPSO算法利用量子系统较大的搜索范围,并借鉴遗传算法的选择、变异等操作,从而避免了算法过早收敛至局部最优,且能得到可观的收敛速度。实验结果表明,GQPSO算法具有更快的收敛速度和全局搜索能力,提高了属性约简的效率。
To solve the problems of the poor convergence speed and being easy to fall into the local optimum in the particle swarm algorithm,an attribute reduction algorithm based on genetic quantum particle swarm（GQPSO） is presented.GQPSO takes advantage of the wide search range of quantum system and utilizes the selection and variation of the genetic algorithm to avoid algorithm premature convergence local optimum and get considerable convergence speed.The experiment shows that GQPSO has a faster convergence rate and global search capabilities,which improves the efficiency of the attribute reduction.
出处
《湖南工业大学学报》
2010年第6期49-52,共4页
Journal of Hunan University of Technology
关键词
属性约简
遗传算法
量子
粒子群
收敛
attribute reduction
genetic algorithm
quantum
particle swarm
convergence
