摘要
数据流分配问题是典型的NP问题,为了有效地解决异构机群下数据流分配问题,提出一种基于改进粒子群优化算法的自适应分配策略.基于生物学的基因理论设计了转基因算子,以保护最优个体并提高策略的局部求解能力;引入变异算子,在很好地保持种群多样性的同时提高策略的全局搜索能力.仿真实验结果表明,文中策略在局部求解与全局探索之间取得了较好的平衡,能够在较短的时间内取得满意的解.
Data streams scheduling is a typical NP-complete problem. To solve the problem of data streams scheduling on heterogeneous cluster effectively, a self-adaptive strategy based on improved particle swarm optimization is proposed. Inspired by the gene theory, a transgenic operator is designed to keep the best individual and improve the ability of local solution. The mutation operator is built into the proposed strategy to maintain population diversity and improve the ability of global exploration. Simulation results show that the proposed strategy gives a good balance between local solution and global exploration and has excellent efficiency in data streams scheduling.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2009年第8期1175-1181,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(60673161)
教育部科学技术研究重点项目(206073)
福建省自然科学基金重点项目(A0820002)
福建省自然科学基金(A0610012)
关键词
异构机群
数据流分配
粒子群优化
转基因算子
变异算子
heterogeneous cluster
data streams scheduling
particle swarm optimization
transgenic operator
mutation operator
