摘要
为改善标准遗传算法的求解效率,提出一种基于6模糊控制器的并行多种群自适应遗传算法。利用MPI(Messagepassinginterface)技术建立了一个COW(Clusterofworkstation)集群,将算法在该硬件平台上进行了实现。3机COW集群的仿真实验结果在演示算法设计可行性的同时,表明该算法的求解效率明显优于用于对照的单种群算法,具有在解决组合优化问题上广泛应用的可能。本文还对影响并行算法的参数进行了探讨。
To improve the solving efficiency of standard genetic algorithms, a novel parallel multi-deme adaptive genetic algorithm is proposed based on six fuzzy logic controllers （6FLC-MDPFGA）. A PC cluster of workstation （COW） using the message passing interface （MPI） technology is built. Furthermore, the 6FLC-MDPFGA is realized on the hardware platform. When the possibility of the algorithm design is illustrated, results from initial experiments on the 3 PCs COW platform indicate that the algorithm efficiency is improved. Meanwhile, experiments show that the new multi-deme algorithm can provide more stable results. The algorithm is run on the 3 PCs COW platform, and easily implemented on a large-scale PCs COW platform using MPI. The 6FLC-MDPFGA can be applied to a wide range of combinatorial optimization. Finally, how to select parameters is discussed.
出处
《南京航空航天大学学报》
EI
CAS
CSCD
北大核心
2005年第6期760-765,共6页
Journal of Nanjing University of Aeronautics & Astronautics
基金
国防基础科研资金资助项目
关键词
并行遗传算法
自适应参数控制
COW集群
MPI
parallel genetic algorithm
adaptive parameter control
cluster of workstation （COW）
message passing interface （MPI）
