改进的离散混合蛙跳算法压缩感知信号重构及应用

doi:10.13229/j.cnki.jdxbgxb201604036

摘要/Abstract

摘要： 针对工程离散优化问题特点,定义了具有普遍意义的青蛙编码方式,设计了编码位调换更新机制,提出了自适应权重因子和双模子族群策略。在此基础上,将改进的离散混合蛙跳算法(Discrete shuffled frog leaping algorithm,DSFLA)应用于压缩感知重构算法中,将未知重构信号理解为青蛙编码方式,利用DSFLA算法全局寻优能力得到次最优信号重构信息,从而实现了稀疏度未知情况下的信号重构。最后对典型TSP(Travelling salesman problem)问题算例和WSNs多目标定位问题进行仿真,仿真结果表明:改进的DSFLA具有更强的复杂问题求解能力,基于改进DSFLA压缩感知重构算法的WSNs目标定位精度优于传统信号重构算法,且抗噪能力达到25~45 dB。

关键词: 计算机应用, 无线传感器网络, 离散混合蛙跳算法, 压缩感知重构算法, 多目标定位

Abstract: An improvement of Discrete Shuffled Frog-leaping Algorithm (DSFLA) is proposed. Fist, according to the characteristics of discrete optimization problems, the frog coding of universal significance is defined, which is important for DSFLA in solving discrete optimization effectively. Then, the update mechanism based on “swapping of coded bits” for DSFLA is designed, and an adaptive weighting factor and sub-ethic dual strategy are presented. Finally, the improved DSFLA is applied in compressed sensing reconstruction algorithm, in which the unknown reconstructed signal encoding is taken as the frog code. Typical TSP problems and multiple target localization in WSN are simulated. Simulation results show that the improved DSFLA has prominent ability to solve complex problems, and the perception accuracy of WSNs target reconstruction based on improved DSFLA CS reconstruction algorithm is better than that of the traditional signal reconstruction algorithm, and the anti noise capacity reaches 25~45 dB.

Key words: computer application, wireless sensor networks, discrete shuffle frog leaping algorithm, compressed sensing reconstruction algorithm, multiple target localization

中图分类号:

TP393

刘洲洲, 王福豹. 改进的离散混合蛙跳算法压缩感知信号重构及应用[J]. 吉林大学学报(工学版), 2016, 46(4): 1261-1268.

LIU Zhou-zhou, WANG Fu-bao. Improvement of discrete shuffled frog-leaping algorithm and application in compressed sensing reconstruction[J]. 吉林大学学报(工学版), 2016, 46(4): 1261-1268.

参考文献

[1] 任哲,周本达,陈明华. 一种基于随机化均匀设计点集的遗传算法用于求解MVCP[J].模式识别与人工智能,2010,23(2):284-288.
Ren Zhe,Zhou Ben-da,Chen Ming-hua. Genetic algorithm for solving a random set of uniform design point MVCP[J] Pattern Recognition and Artificial Intelligence, 2010,23 (2): 284-288.
[2] Eusuff M M, Lansey K E. Optimization of water distribution network design using the shuffled frog leaping algorithm[J]. Water Resources Planning and Management, 2003, 129(3): 210-225.
[3] 李建军,郁滨,陈武平. 混合蛙跳算法的改进与仿真[J]. 系统仿真学报,2014,26(4):755-760.
Li Jian-jun,Yu Bin,Chen Wu-ping. SFLA Improvement and simulation[J]. Journal of System Simulation, 2014, 26(4): 755-760.
[4] 骆剑平,李霞,陈泯融. 基于改进混合蛙跳算法的CVRP求解[J]. 电子与信息学报,2011,33(2): 429-434.
Luo Jian-ping,Li Xia,Chen Min-rong. Obliterate the CVRP solving based on SFLA[J]. Journal of Electronics and Information Technology, 2011, 33 (2): 429-434.
[5] 肖莹莹,柴旭东,李伯虎,等. 混合蛙跳算法的收敛性分析及其改进[J]. 华中科技大学学报:自然科学版,2012,40(7):15-18.
Xiao Ying-ying,Chai Xu-dong,Li Bo-hu, et al. Convergence of SFLA its improvement[J]. Huazhong University of Science and Technology (Natural Science), 2012,40(7): 15-18.
[6] Donoho D. Compressed sensing[J]. IEEE Transactions on Information Theory,2006,52(4): 1289-1306.
[7] 孔繁锵,井庆丰,计振兴. 图像压缩感知的自适应方向提升稀疏表示及重构算法[J]. 宇航学报,2013,34(1):121-127.
Kong Fan-qiang,Jing Qing-feng,Ji Zhen-xing. Revitalization of adaptive image compression direction to enhance the perception sparse representation and reconstruction algorithm[J]. Journal of Astronautics, 2013,34 (1): 121-127.
[8] 练秋生,陈书贞. 基于解析轮廓波变换的图像稀疏表示及其在压缩传感中的应用[J]. 电子学报,2010,38(6):1293-1298.
Lian Qiu-sheng Chen Shu-zhen. Representation image sparse profile based on analytic wavelet transform in compressed sensing[J]. Journal of Electronics, 2010,38(6):1293-1298.
[9] Peyré G. Best basis compressed sensing[J]. IEEE Transactions on Signal Processing, 2010, 58(5): 2613-2622.
[10] 彭向东,张华,刘继忠. 基于过完备字典的体域网压缩感知心电重构[J]. 自动化学报,2014,40(7):1421-1432.
Peng Xiang-dong, Zhang Hua, Liu Ji-zhong. Had complete dictionary based on body area network compressed sensing cardiac remodeling[J]. Automatica Sinica, 2014,40 (7): 1421-1432.
[11] 张宗念,黄仁泰,闫敬文. 压缩感知信号盲稀疏度重构算法[J]. 电子学报, 2011, 39(1): 18-22.
Zhang Zong-nian, Huang Ren-tai, Yan Jing-wen. Compressed sensing reconstruction algorithm for blind signal sparsity[J]. Acta Electronica, 2011, 39 (1): 18-22.
[12] 刘洲洲,王福豹. 基于离散萤火虫压缩感知重构的无线传感器网络多目标定位[J]. 光学精密工程,2014,22(7):1904-1911.
Liu Zhou-zhou, Wang Fu-bao. Reconstruction discrete wireless sensor networks multi-objective positioning based on compressed sensing[J]. Optics and Precision Engineering, 2014,22 (7): 1904-1911.
[13] 周永权,黄正新,刘洪霞. 求解TSP问题的离散型萤火虫群优化算法[J]. 电子学报,2012,40(6):1164-1170.
Zhou yong-quan,Huang Zheng-xin,Liu Hong-xia. Discrete glowworm swarm optimization algorithm for solving TSP[J]. Acta Electronica Sinica, 2012, 40(6): 1164-1170.
[14] 姜建国,张丽媛,苏仟,等. 一种利用动态搜索策略的混合蛙跳算法[J]. 西安电子科技大学学报:自然科学版,2014,41(4):51-57.
Jiang Jian-guo,Zhang Li-yuan,Su Qian, et al. A shuffled frog leaping algorithm by dynamic search strategy[J]. Journal of Xi'an Electronic and Science University (Natural Science Edition), 2014, 41(4): 51-57.
[15] 郑仕链,楼才义,杨小牛. 基于改进混合蛙跳算法的认知无线电协作频谱感知[J]. 物理学报,2010,59(5):3611-3616.
Zheng Shi-lian,Lou Cai-yi, Yang Xiao-niu. An improved shuffled frog leaping algorithm for cooperative spectrum sensing in cognitive radio networks based on[J]. Acta Physica Sinica, 2010, 59 (5): 3611-3616.
[16] 石光明,刘丹华,高大化,等. 压缩感知理论及其研究进展[J]. 电子学报,2009,37(5):1070-1081.
Shi Guang-ming, Liu Dan-hua, Gao Da-hua, et al. Theory and research progress compressed sensing[J]. Journal of Electronics, 2009,37 (5): 1070-1081.
[17] 何风行,余志军,刘海涛. 基于压缩感知的无线传感器网络多目标定位算法[J]. 电子与信息学报,2012,34(3):716-721.
He Feng-xing,Yu Zhi-jun,Liu Hai-tao. Wireless sensor networks based on the compressed sensing in multi target locating algorithm[J]. Journal of Electronics and Information Technology, 2012, 34 (3): 716-721.
[18] 黄国锐,曹先彬,王煦法. 基于信息素扩散的蚁群算法[J]. 电子学报,2004,32(5):865-868.
Huang Guo-rui, Cao Xian-bin, Wang Xu-fa. Pheromone diffusion method based on ant colony algorithm[J]. Journal of Electronics, 2004,32(5): 865-868.

相关文章 15

[1]	刘富,宗宇轩,康冰,张益萌,林彩霞,赵宏伟. 基于优化纹理特征的手背静脉识别系统[J]. 吉林大学学报(工学版), 2018, 48(6): 1844-1850.
[2]	王利民,刘洋,孙铭会,李美慧. 基于Markov blanket的无约束型K阶贝叶斯集成分类模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1851-1858.
[3]	金顺福,王宝帅,郝闪闪,贾晓光,霍占强. 基于备用虚拟机同步休眠的云数据中心节能策略及性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1859-1866.
[4]	赵东,孙明玉,朱金龙,于繁华,刘光洁,陈慧灵. 结合粒子群和单纯形的改进飞蛾优化算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1867-1872.
[5]	刘恩泽,吴文福. 基于机器视觉的农作物表面多特征决策融合病变判断算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1873-1878.
[6]	欧阳丹彤, 范琪. 子句级别语境感知的开放信息抽取方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1563-1570.
[7]	刘富, 兰旭腾, 侯涛, 康冰, 刘云, 林彩霞. 基于优化k-mer频率的宏基因组聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1593-1599.
[8]	桂春, 黄旺星. 基于改进的标签传播算法的网络聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1600-1605.
[9]	刘元宁, 刘帅, 朱晓冬, 陈一浩, 郑少阁, 沈椿壮. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1606-1613.
[10]	车翔玖, 王利, 郭晓新. 基于多尺度特征融合的边界检测算法[J]. 吉林大学学报(工学版), 2018, 48(5): 1621-1628.
[11]	赵宏伟, 刘宇琦, 董立岩, 王玉, 刘陪. 智能交通混合动态路径优化算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[12]	黄辉, 冯西安, 魏燕, 许驰, 陈慧灵. 基于增强核极限学习机的专业选择智能系统[J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[13]	傅文博, 张杰, 陈永乐. 物联网环境下抵抗路由欺骗攻击的网络拓扑发现算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[14]	曹洁, 苏哲, 李晓旭. 基于Corr-LDA模型的图像标注方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
[15]	侯永宏, 王利伟, 邢家明. 基于HTTP的动态自适应流媒体传输算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1244-1253.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed