基于非线性因子的改进鸟群算法在动态能耗管理中的应用

罗钧; 刘泽伟; 张平; 刘学明; 柳政

doi:10.11999/JEIT190264

基于非线性因子的改进鸟群算法在动态能耗管理中的应用

doi: 10.11999/JEIT190264

罗钧^1, ,,
刘泽伟¹,
张平²,
刘学明²,
柳政²

1.
重庆大学光电技术及系统教育部重点实验室重庆 400030
2.
兵器工业 5011 区域计量站重庆 400050

基金项目: 国防科工局十二五(跨十三五)技术基础科研项目(JSJL2014209B004, JSJL2014209B005)

详细信息

作者简介:
罗钧：男，1963年生，教授，博士生导师，研究方向为模式识与人工智能、精密机械及测试计量、智能信息处理

刘泽伟：男，1994年生，硕士生，研究方向为嵌入式系统、精密仪器及机械、测试计量技术及仪器

张平：男，1970年生，硕士生，研究方向为精密仪器及机械、测试计量技术及仪器

刘学明：男，1963年生，硕士生，研究方向为精密仪器及机械、测试计量技术及仪器

通讯作者:
罗钧　luojun@cqu.edu.cn

中图分类号: TP316.7
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- 被引次数: 0
出版历程
- 收稿日期: 2019-04-18
- 修回日期: 2019-10-08
- 网络出版日期: 2019-10-16
- 刊出日期: 2020-03-19

Application of Improved Bird Swarm Algorithm Based on Nonlinear Factor in Dynamic Energy Management

1.
Key Laboratory of Optoelectronic Technology and System of Ministry of Education, Chongqing University, Chongqing 400030, China
2.
5011 District Measurement Station of Weapon Industry, Chongqing 400050, China

Funds: The Science, Technology and Industry Bureau for National Defense 12th Five-year (13th Five-year) Basic Technology Research Projects (JSJL2014209B004, JSJL2014209B005)

摘要

摘要:
针对实时系统能耗管理中动态电压调节(DVS)技术的应用会导致系统可靠性下降的问题，该文提出一种基于改进鸟群(IoBSA)算法的动态能耗管理法。首先，采用佳点集原理均匀地初始化种群，从而提高初始解的质量，有效增强种群多样性；其次，为了更好地平衡BSA算法的全局和局部搜索能力，提出非线性动态调整因子；接着，针对嵌入式实时系统中处理器频率可以动态调整的特点，建立具有时间和可靠性约束的功耗模型；最后，在保证实时性和稳定性的前提下，利用提出的IoBSA算法，寻求最小能耗的解决方案。通过实验结果表明，与传统BSA等常见算法相比，改进鸟群算法在求解最小能耗上有着很强的优势及较快的处理速度。
- 能耗管理 /
- 实时系统 /
- 动态电压调节 /
- 改进鸟群算法
Abstract:
The application of Dynamic Voltage Scaling (DVS) technique in real-time system energy management will result in the decrease of system reliability. A dynamic energy management method based on Improved Bird Swarm Algorithm (IoBSA) is proposed in this paper. Firstly, the population is initialized uniformly with the principle of good point set, so as to improve the quality of initial solution and increase the diversity of population effectively. Secondly, in order to balance better the global and local search ability of BSA algorithm, the nonlinear dynamic adjustment factor is proposed. Then, a power consumption model with time and reliability constraints is established for the dynamic adjustment of processor frequency in embedded real-time systems. On the premise of ensuring real-time performance and stability, the proposed IoBSA algorithm is used to find the solution with minimum energy consumption. The experimental results show that compared with the traditional BSA algorithm and other common algorithms, the improved bird swarm algorithm has a strong advantage in solving the minimum energy consumption and a fast processing speed energy management.
- Energy management /
- Real-time system /
- Dynamic Voltage Scaling (DVS) /
- Improved Bird Swarm Algorithm (IoBSA)

HTML全文

图 1 两种方法初始化点图

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图 2 频率故障率

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图 3 收敛曲线图

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表 1 部分算法参数列表

算法	参数设置
BSA	$C = S = 1.5,{a_1} = {a_2} = 1,{\rm FQ} = 5,P \in [0.8,\,1]$ ${\rm FL} \in [0.5,\,0.9]$
LSABSA	${a_1} = {a_2} = 1,{\rm FQ} = 5,P \in [0.8,\,1],{\rm FL} \in [0.5,\,0.9]$ ${C_{\rm{e}}} = {S_{\rm{s}}} = 0,5,{C_{\rm{s}}} = {S_{\rm{e}}} = 2.5$
本文	${a_1} = {a_2} = 1,{\rm FQ} = 5,P \in [0.8,1],{\rm FL} \in [0.5,\,0.9]$
IoBSA	${C_{\rm{e}}} = {S_{\rm{s}}} = 0,5,{C_{\rm{s}}} = {S_{\rm{e}}} = 2$
CBSA	${Q_{\min }} = 0,{Q_{\max }} = 2,A = 0.7,r = 0.4,{P_\alpha } = 0.25$
CJADE	$F = 0.8,{C_r} = 0.5,c = 0.1,p = 0.05$
文献[10]	${\rm{limit}} = 50$

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表 2 实验参数列表

参数名	值	参数名	值
种群数	60	任务量	10 30 50
归一化频率	0.1～1.0	截止时间	20～220
WCET	20～50	迭代次数	1000
运行次数	20	惩罚因子	5000

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表 3 任务量为10的优化结果

NPM-Val	St.	BSA	本文IoBSA	LSABSA	CSBA	GWO	CJADE	文献[10]
375.57	Best	853.45	821.52	896.57	1040.55	830.83	904.09	1187.05
(min)	Worst	1110.96	1040.01	1090.47	1178.55	1053.47	1123.84	1061.25
3427.05	Mean	967.95	913.04	1005.06	1105.57	964.94	1035.83	1147.21
(max)	Std.Dev	58.18	57.66	60.36	34.85	50.46	53.92	55.25

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表 4 任务量为30的优化结果

NPM-Val	St.	BSA	本文IoBSA	LSABSA	CSBA	GWO	CJADE	文献[10]
1126.70	Best	4355.13	3642.20	4197.41	4048.74	4353.49	4382.29	4881.90
(min)	Worst	5158.38	4936.64	5175.33	5033.73	5234.853	5021.29	5470.92
10281.15	Mean	4771.52	4368.30	4739.58	4519.13	4681.22	4677.56	4928.57
(max)	Std.Dev	215.87	345.31	269.02	238.77	223.95	150.11	304.62

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表 5 任务量为50的优化结果

NPM-Val	St.	BSA	本文IoBSA	LSABSA	CSBA	GWO	CJADE	文献[10]
1877.83	Best	8572.38	8281.54	8610.62	无效	8384.88	8416.94	无效
(min)	Worst	10442.74	10023.18	10149.21	无效	无效	无效	无效
17135.25	Mean	9557.82	9319.57	9513.31	无效	无效	无效	无效
(max)	Std.Dev	587.00	535.50	520.50	643448.64	529852.01	75029.97	1147609.95

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