摘要
粒子群算法是一种基于群体智能的随机搜索优化方法,能够有效的优化各类函数.提出了具有时间因子的粒子群优化算法,其目的在于克服基本粒子群算法的早熟与收敛速度慢等缺点.数值试验表明,具有时间因子的粒子群优化算法收敛速度更快,运算达到的精度更高,运行更为稳定,并能有效克服局部极值.
Particle swarm optimization algorithm is random search optimization algorithm on the basis of group intelligence,which can effectively optimize the various functions.To overcome the premature and slow convergence,an improved particle swarm optimization is presented by inducing a time factor to adjust the convergence rate.Numerical experiments show that the improved algorithm not only has faster convergence,more accurate and stable,but also avert the local extreme values effectively.
出处
《纺织高校基础科学学报》
CAS
2011年第2期303-308,共6页
Basic Sciences Journal of Textile Universities
基金
国家自然科学基金资助项目(60671063
10902062)
关键词
粒子群
进化算法
时间因子
智能计算
particle swarm
evolution algorithms
time factor
computational intelligence