基于改进型象群优化算法的BSS方法  

BSS method based on improved elephant herding optimization algorithm

作　　者：夏奎 李炜[1,2] 邱意敏[1,2] 王晶 Xia Kui;Li Wei;Qiu Yimin;Wang Jing(Anhui Key Laboratory of Detection Technology and Energy Saving Devices,Anhui Polytechnic University,Wuhu 241000,China;Key Laboratory of Advanced Perception and Intelligent Control for High-end Equipment,Ministry of Education,Anhui Polytechnic University,Wuhu 241000,China)

机构地区：[1]安徽工程大学检测技术与节能装置安徽省重点实验室,芜湖241000 [2]安徽工程大学高端装备先进感知与智能控制教育部重点实验室,芜湖241000

出　　处：《电子测量与仪器学报》2021年第10期153-160,共8页Journal of Electronic Measurement and Instrumentation

基　　金：安徽工程大学检测技术与节能装置安徽省重点实验室开放研究基金资助项目(2017070503B026-A04);安徽省省级质量工程“六卓越、一拔尖”卓越人才培养创新项目(2020zyrc039);安徽省省级质量工程教学研究一般项目(2020jyxm1371);安徽工程大学校级本科教学质量提升计划研究项目(2020jyxm16);安徽高校自然科学研究项目(KJ2020A0351);安徽高校自然科学研究项目(KJ2020A0345);安徽工程大学科研项目(Xjky2020031)资助。

摘　　要：为了克服传统群智能算法在求解盲源分离(BSS)问题时收敛速度慢和分离精度差的缺点,提出一种基于改进型象群优化(IEHO)算法的BSS方法。该方法利用独立性原则,融合分离信号的峭度和互信息来构建目标函数。在氏族更新阶段,通过改进算法比例因子并加入邻域搜索,提高了算法搜索方式的多样性;在分离阶段,引入量子粒子群优化策略,提高了算法的全局搜索能力。仿真结果表明,与传统的象群优化算法和粒子群优化算法相比,IEHO算法的寻优效果较好,并成功实现了图像信号和语音信号的盲源分离,分离精度更高,收敛速度更快。In order to overcome the disadvantages of slow convergence speed and poor separation accuracy when solving blind source separation(BSS)problems in traditional swarm intelligence algorithms,a BSS method based on improved elephant herding optimization(IEHO)is proposed.The method uses the principle of independence to construct the objective function,combining the kurtosis and the mutual information of separating signals.In the clan update stage,the scale factor of algorithm is modified as well as exploiting the neighborhood search to improve the diversity of the search.In the separation stage,the quantum-behaved particle swarm optimization strategy is introduced to improve the global search ability of the algorithm.The simulation results show that the IEHO algorithm has a better optimization effect compared with the traditional elephant herding optimization algorithm and the particle swarm optimization algorithm.Meanwhile,the proposed algorithm can also realize blind source separation of images and speech successfully,with higher separation accuracy and faster convergence speed.

关 键 词：盲源分离 象群优化 邻域搜索 量子粒子群优化 

分 类 号：TN911.7[电子电信—通信与信息系统]