摘要
为了提高算法的有效性,利用梯度算法和粒子群算法独立的运行机制,采用驱赶技术和重新初始化部分群体的技术,提出了一种基于梯度下降法和粒子群算法的两阶段优化算法,并对新算法进行了理论分析和数值仿真.数值结果显示新算法比单纯梯度算法有更好的全局优化能力,比单纯粒子群算法有更快的收敛速度和更高的精度.新算法求解质量更高,运行更稳定.
To enhance effectiveness of algorithm, on the basis of analyzing the independent operating mecha- nism of both gradient algorithm and particle swarm algorithm, a two-phase optimization algorithm based on gradi- ent descent and particle swarm algorithm is presented; it adopts the driving technique and the re-initialization tech- nique of part of population. Then, the theoretical analysis and numerical simulation about the new algorithm are made. The numerical simulation shows this new algorithm has better global optimization ability than the gradient algorithm, and it has faster convergences speed and lighter solution accuracy than particle swarm algorithm. This new algorithm produces a lighter quality solution and has more stable operation.
出处
《河北大学学报（自然科学版）》
CAS
北大核心
2012年第2期207-211,共5页
Journal of Hebei University(Natural Science Edition)
基金
河北省软科学研究计划项目(11457250)
河北省自然科学基金资助项目(F2009000236)
关键词
全局优化
两阶段算法
梯度算法
粒子群算法
global optimization
two-phase algorithm
gradient algorithm
particle swarm optimization
