微型机与应用Issue(11)：67-70,74,5.
不同拓扑结构的并行粒子群优化算法的实现
Realization of parallel particle swarm optimization algorithm based on different neighborhood topologies
摘要
Abstract
Neighborhood topology has an important influence on the performance of particle swarm optimization algorithm. The algorithm for solving optimization problems on the CPU is very inefficient. For these two point, analyzing parallel characteristic of PSO algorithm when neighborhood topology changes and achieving a ring and star topologies PSO algorithm on compute unified device architecture(CUDA) on the optimization process. Solving 7 benchmark test functions on the CPU and the GPU PSO algorithm respectively, the program simulation results show that PSO algorithm based on CUDA computing efficiency is significantly higher than CPU. In the meantime, GPU accelerates dramatically star PSO algorithm convergence speed, while the ring structure PSO algorithm have little effect.关键词
粒子群优化算法/统一计算设备架构/邻域拓扑结构/计算效率Key words
particle swarm optimization/Compute Unified Device Architecture/neighborhood topology/computational efficiency分类
信息技术与安全科学引用本文复制引用
张科,高晓智..不同拓扑结构的并行粒子群优化算法的实现[J].微型机与应用,2014,(11):67-70,74,5.基金项目
芬兰科学院基金 （）
