基于采样尺度自适应的多尺度量子谐振子优化算法并行化

A scale-adaptive multi-scale quantum harmonic oscillator algorithm and its parallelization

多尺度量子谐振子优化算法MQHOA是基于量子波函数理论提出的元启发式算法,传统MQHOA寻优过程中不同个体的采样尺度不具有差异性,这种机制限制了解的多样性。针对适应度不同的采样个体,提出采样尺度自适应策略,将采样情况差的个体采样尺度合理扩大,增加迭代过程中不同采样个体所使用采样尺度的多样性,并基于采样尺度的差异性提出并行化框架。选取7组测试函数将改进后的算法(MQHOA-PS)与MQHOA在华为鲲鹏920和AMD EPYC 7452处理器上进行测试实验,实验结果表明,改进后的算法寻优具有较高的精度和成功率,并且所需时间更短。

Multi-scale quantum harmonic oscillator algorithm(MQHOA)is a meta-heuristic algorithm based on the theory of Quantum wave function.In the traditional MQHOA optimization process,the sampling scale of different individuals is not different.This mechanism limits the diversity of solutions.Aiming at the sampled individuals with different fitness levels,a scale adaptive strategy is proposed.This strategy reasonably expands the scale of individuals with poor sampling conditions and increases the diversity of sampling scales used by different individuals in the iterative process.In addition,a parallelization framework is proposed based on the scale difference.Seven groups of test functions are selected to test the improved algorithm(MQHOA-PS)and MQHOA on the Huawei Kunpeng 920 processor and AMD EPYC 7452 processor.The experiments show that the improved algorithm has higher accuracy and success rate and less time.