摘要
针对基本萤火虫群优化算法在求解多极值函数问题时,随着极值点增多,收敛速度低、精度不高的缺陷,提出了一种小规模多种群的改进萤火虫群算法,实验仿真表明,改进后的萤火虫群算法在求解多极值函数优化问题时,所花时间明显减少且精度也得到了提高。
With the increase of the extreme points,the convergence speed and the computing accuracy of the Glowworm Swarm Optimization（GSO）algorithm are low and not high.Aiming at the shortcomings of the GSO algorithm,this paper proposes a new improved algorithm of small-scale and Multi-Population Glowworm Swarm Optimization（MPGSO）.It is shown by simulation that,compared with GSO,the improved algorithm for solving multi-modal functions can not only obviously reduce the computing time,but also improve the computing accuracy.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第23期48-50,共3页
Computer Engineering and Applications
基金
广西自然科学基金(No.桂科自0728054)
关键词
萤火虫群优化算法
函数优化
智能算法
多峰函数
Glowworm Swarm Optimization（GSO）
function optimization
intelligent algorithm
multi-modal function
