基于历史认知的鲸鱼算法求解动态能耗  

Whale algorithm based on historical cognition for solving dynamic energy consumption

作　　者：罗钧[1] 庞亚男 刘建强 Luo Jun;Pang Yanan;Liu Jianqiang(Key Laboratory of Optoelectronic Technology and System of Ministry of Education,Chongqing University,Chongqing 400030,China;Sichuan Aerospace Electronic Equipment Research Institute,Chengdu 610100,China)

机构地区：[1]重庆大学光电技术及系统教育部重点实验室,重庆400030 [2]四川航天电子设备研究所,成都610100

出　　处：《电子测量与仪器学报》2022年第1期236-245,共10页Journal of Electronic Measurement and Instrumentation

基　　金：国防科工局十二五技术基础科研项目(JSJL2014209B005);工信部“两机”重大专项基础研究项目(Z20210208)资助

摘　　要：为提高嵌入式实时系统的能耗管理效率,降低传统动态电压缩放对系统稳定性的影响,提出了基于历史认知的鲸鱼算法支持下的动态能耗优化方案。首先提出非线性动态控制收敛因子的策略,有效加快了算法收敛速度。其次采用历史最优解作为收缩包围机制中的种群干扰因子,设计了混合引导策略来平衡算法的局部开发和全局搜索能力。最后根据动态电压缩放技术可以实时改变处理器频率的特征,利用改进算法对任务量10、30和50进行优化,验证了改进算法的有效性。In order to improve the energy consumption management efficiency of the embedded real-time system and reduce the impact of traditional dynamic voltage scaling technology on system stability,a dynamic energy consumption optimization scheme supported by whale algorithm based on historical cognition is proposed.Firstly,a nonlinear dynamic convergence factor control strategy is proposed,which can effectively accelerate the convergence speed of the algorithm.Secondly,using the historical optimum solutions as interference factors,a hybrid guided strategy is designed in the constriction and envelopment mechanism to balance the local development and global search capability of the algorithm.Finally,the frequency characteristics of the processor can be changed in real time according to the dynamic voltage scaling technology,the tasks 10,30 and 50 are optimized by the algorithm,so as to verify the effectiveness of the improved algorithm.

关 键 词：能耗管理 动态电压缩放 历史认知 非线性收敛因子 改进鲸鱼算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP332[自动化与计算机技术—控制科学与工程]