摘要
本文通过对传统粒子群算法(PSO)的分析,在GPU(Graphic Process Unit)上设计了基于一般反向学习策略的粒子群算法,并用于求解大规模优化问题.主要思想是通过一般反向学习策略转化当前解空间,提高算法找到最优解的几率,同时使用GPU大量线程并行来加速收敛速度.对比数值实验表明,对于求解大规模高维的优化问题,本文算法比其他智能算法具有更好的精度和更快的收敛速度.
Through an analysis of the traditional particle swarm algorithm, this paper presents particle swarm algorithm based on the generalized opposition-based particle （GOBL） swarm algorithm on Graphic Processing Unit （GPU）, and applies it to solve large scale optimization problem. The generalized opposi- tion learning strategies transforms the current solution space to provide more chances of finding better so- lutions, and GPU in parallel accelerates the convergence rate. Experiment shows that this algorithm has better accuracy and convergence speed than other algorithm for solving large-scale and high-dimensional problems.
出处
《武汉大学学报（理学版）》
CAS
CSCD
北大核心
2011年第2期148-154,共7页
Journal of Wuhan University:Natural Science Edition
基金
国家自然科学基金项目(61070008)
江西省科技支撑项目(2009BHB16400)
山西省高校基金资助项目(200900294)
关键词
粒子群算法
反向策略
图形处理器
高维优化问题
particle swarm optimization
opposition-based learning
Graphic Process Unit
high dimensional optimization
