多尺度量子谐振子优化算法的并行性研究

摘要

多尺度量子谐振子优化算法(MQHOA, multi-scale quantum harmonic oscillator algorithm)是一种利用量子谐振子波函数构造的新的智能算法，采样运算是MQHOA算法的基本运算单元和主要运算量，采样运算的独立性赋予MQHOA算法内在并行性。通过对MQHOA算法群体参数和采样参数进行实验，确定算法的并行粒度并提出多尺度量子谐振子并行算法（MQHOA-P, multi-scale quantum harmonic oscillator parallel algorithm）。在由10个计算节点构成的集群上对6种标准测试函数进行实验，通过改变计算节点数、函数维数和采样参数测试MQHOA-P算法的加速比，实验结果表明，MQHOA-P算法具有良好的加速比和扩展性，可以在大规模集群中部署、运行。%MQHOA was a novel intelligent algorithm constructed by quantum harmonic oscillator's wave function. Sam-pling was the basic operation and main computational burden of MQHOA. The independence of sampling operation con-structs MAHOA’s parallelism. Parallel granularity was obtained by experiments of group parameter and sampling pa-rameter, and MQHOA-P was proposed. Experiments were done in a cluster of ten nodes on six standard test functions. By changing node number, function dimension and sampling parameter, experiments of MQHOA-P’s speed-up ratio were done. The experimental results show the good performance of MQHOA-P’s speed-up ratio and expansibility. MQHOA-P can be deployed and run on multiple nodes in a large-scale cluster.