计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 277-291.doi: 10.11896/jsjkx.220300269
孙林, 李梦梦, 徐久成
SUN Lin, LI Mengmeng, XU Jiucheng
摘要： 针对哈里斯鹰优化(Harris Hawk Optimization,HHO)算法在探索阶段仅使用随机策略初始种群,致使种群多样性下降,控制开发和探索过程中的线性变化的逃逸能量,在迭代后期易陷入局部最优等问题,提出了二进制HHO及其元启发式特征选择算法。首先,在探索阶段引入Sine映射函数,初始化哈里斯鹰种群位置,运用自适应调整算子来改变HHO搜索范围,并更新HHO的种群位置。其次,利用对数惯性权重改进逃逸能量的更新公式,将迭代次数引入跳跃距离中,使用步长调整参数调整HHO的搜索距离,进而平衡探索与开发能力;在此基础上设计了改进的HHO算法,避免HHO算法陷入局部最优。然后,引入S型和V型传递函数,更新改进的HHO算法的二进制位置和种群位置,设计了两种二进制的改进HHO算法。最后,使用适应度函数评估特征子集,并将二进制改进HHO算法与适应度函数相结合,提出了两种基于二进制的改进HHO元启发式特征选择算法。在10个基准函数和17个公共数据集上的实验结果表明,4种优化策略在10个基准函数上有效提升了HHO算法的优化性能,改进的HHO算法明显优于对比的其他优化算法;在12个UCI数据集和5个高维基因数据集上,将所提算法与基于BHHO的特征选择算法和其他特征选择算法进行比较,实验结果显示,基于V型改进的HHO特征选择算法具备良好的寻优能力与分类性能。
中图分类号:
| [1]SUN L,LI M M,DING W P,et al.AFNFS:Adaptive fuzzy neighborhood-based feature selection with adaptive synthetic over-sampling for imbalanced data[J].Information Sciences,2022,612:724-744. [2]TENG J Y,GAO M,ZHENG X M,et al.Noise tolerable feature selection method for software defect prediction[J].Computer Science,2021,48(12):131-139. [3]LIU Y,CHENG L,SUN L.Feature selection method based on K-S test and neighborhood rough sets[J].Journal of Henan Normal University(Natural Science Edition),2019,47(2):21-28. [4]SUN L,HUANG M M,XU J C.Weak label feature selectionmethod based on neighborhood rough sets and relief[J].Computer Science,2022,49(4):152-160. [5]RUAN Z H,XIAO X Y,HU W X,et al.Multiple power quality disturbance classification feature optimization based on multi-granularity feature selection and model fusion[J].Power System Protection and Control,2022,50(14):1-10. [6]SUN L,YIN T Y,DING W P,et al.Feature selection with mis-sing labels using multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy[J].IEEE Transactions on Fuzzy Systems,2022,30(5):1197-1211. [7]LIU Q Z,HUANG C S.Optimization of dielectric responsecharacteristics of oil paper insulation based on FCBF feature selection and the XGBoost principle[J].Power System Protection and Control,2022,50(15):50-59. [8]SUN L,ZHAO J,XU J C,et al.Feature selection method based on improved monarch butterfly optimization algorithm[J].Pattern Recognition and Artificial Intelligence,2020,33(11):981-994. [9]DEHKORDI A A,SADIQ A S,MIRJALILI S,et al.Nonlinear-based chaotic harris hawks optimizer:algorithm and internet of vehicles application[J].Applied Soft Computing,2021,109:107574. [10]QIAO B J,ZHANG J X,ZUO X Y.A heuristic independent taskscheduling algorithm based on task execution time[J].Journal of Henan Normal University(Natural Science Edition),2022,50(5):19-28. [11]KANG Y,WANG H N,TAO L,et al.Hybrid improved flower pollination algorithm and gray wolf algorithm for feature selection[J].Computer Science,2022,49(S1):125-132. [12]ZHANG L.Credit evaluation model of small and medium-sized enterprises based on HGA-SVM[J].Journal of Henan Normal University(Natural Science Edition),2022,50(2):79-85. [13]YANG H Z,TIAN F M,ZHANG P,et al.Short-term load forecasting based on CEEMD-FE-AOA-LSSVM[J].Power System Protection and Control,2022,50(13):126-133. [14]LI B X,WAN R Z,ZHU Y J,et al.Multi-strategy comprehensive article swarm optimization algorithm based on population partition[J].Journal of Henan Normal University(Natural Science Edition),2022,50(3):85-94. [15]MIRJALILI S.The ant lion optimizer[J].Advances in Enginee-ring Software,2015,83:80-98. [16]SUN L,WANG X Y,DING W P,et al.TSFNFR:Two-stage fuzzy neighborhood-based feature reduction with binary whale optimization algorithm for imbalanced data classification[J/OL].Knowledge-Based Systems,2022,109849.https://doi.org/10.1016/j.knosys.2022.109849. [17]JI B,LU X Z,SUN G,et al.Bio-inspired feature selection:An improved binary particle swarm optimization approach[J].IEEE Access,2020,8:85989-86002. [18]MAFARJA M,ALJARAH I,FARIS H,et al.Binary grasshopper optimisation algorithm approaches for feature selection problems[J].Expert Systems with Applications,2019,117:267-286. [19]EMARY E,ZAWBAA H M,HASSANIEN A E.Binary grey wolf optimization approaches for feature selection[J].Neurocomputing,2016,172:371-381. [20]HEIDARI A A,MIRJALILI S,FARIS H,et al.Harris hawks optimization:Algorithm and applications[J].Future Generation Computer Systems,2019,97:849-872. [21]TANG A D,HAN T,XU D W,et al.Chaotic elite harris hawk optimization algorithm[J].Journal of Computer Applications,2021,41(8):2265-2272. [22]THAHER T,HEIDARI A A,MAFARJA M,et al.Binary harris hawks optimizer for high-dimensional,low sample size feature selection[M]//Evolutionary Machine Learning Techniques.Algorithms for Intelligent Systems.Singapore:Springer,2020:251-272. [23]ABDEL M,DING W,EL D.A hybrid harris hawks optimization algorithm with simulated annealing for feature selection[J].Artificial Intelligence Review,2021,54(1):593-637. [24]HUSSAIN K,NEGGAZ N,ZHU W,et al.An efficient hybrid sine-cosine Harris Hawks optimization for low and high-dimensional feature selection[J].Expert Systems with Applications,2021,176:114778. [25]YANG X S.Nature-inspired metaheuristic algorithms[M].Luniver Press,2010. [26]BAN D H,LV X,WANG X Y.Efficient image encryption algorithm based on 1D chaotic map[J].Computer Science,2020,47(4):278-284. [27]FANG J Y,JI Y S,ZHAO X C.Opposition-based differentialevolution algorithm with Gaussian distribution estimation[J].Journal of Henan Normal University(Natural Science Edition),2021,49(3):27-32. [28]CHEN L,YIN J S.Whale Swarm Optimization Algorithm based on gaussian difference mutation and logarithmic inertia weight[J].Computer Engineering and Applications,2021,57(2):77-90. [29]KENNEDY J,EBERHART R C.A discrete binary version of the particle swarm algorithm[C]//Proceedings of the 1997 IEEE International Conference on Systems,Man,and Cyberne-tics.Computational Cybernetics and Simulation,1997:4104-4108. [30]HU P,PAN J S,CHU S C.Improved binary grey wolf optimizer and its application for feature selection[J].Knowledge-Based Systems,2020,195:105746. [31]RASHEDI E,NEZAMABADI-POUR H,SARYAZDI S.BGSA:Binary gravitational search algorithm[J].Natural Computing,2010,9(3):727-745. [32]THOM DE SOUZA R C,DE MACEDO C A,DOS SANTOSCOELHO L,et al.Binary coyote optimization algorithm for feature selection[J].Pattern Recognition,2020,107:107470. [33]HASHIM F A,HOUSSEIN E H,MABROUK M S,et al.Henry gas solubility optimization:A novel physics-based algorithm[J].Future Generation Computer Systems,2019,101:646-667. [34]FARAMARZI A,HEIDARINEJAD M,MIRJALILI S,et al.Marine predators algorithm:A nature-inspired metaheuristic[J].Expert Systems with Applications,2020,152:113377. [35]SHAREEF H,IBRAHIM A A,MUTLAG A H.Lightningsearch algorithm[J].Applied Soft Computing Journal,2015,36:315-333. [36]ABD ELAZIZ M,YANG H,LU S.A multi-leader harris hawk optimization based on differential evolution for feature selection and prediction influenza viruses H1N1[J].Artificial Intelligence Review,2022,55:2675-2732. [37]LIU J J,WU C Z,CAO J,et al.A binary differential search algorithm for the 0-1 multidimensional knapsack problem[J].Applied Mathematical Modelling,2016,40(23/24):9788-9805. [38]CHENG S,QIN Q D,CHEN J F,et al.Brain storm optimization algorithm:A review[J].Artificial Intelligence Review,2013,46(4):445-458. [39]MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69:46-61. [40]MIRJALILI S.Dragonfly algorithm:A new meta-heuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J].Neural Computing and Applications,2016,27:1053-1073. [41]SUN L,WANG L Y,QIAN Y H,et al.Feature selection using Lebesgue and entropy measures for incomplete neighborhood decision systems[J].Knowledge-Based Systems,2019,186:104942. [42]JIANG L X,ZHANG L G,LI C Q,et al.A correlation-based feature weighting filter for naive bayes[J].IEEE Transactions on Knowledge and Data Engineering,2019,31(2):201-213. [43]XIONG X X,WANG W W.Kernelized correlation filteringmethod based onfast discriminative scale estimation[J].Journal of Computer Applications,2019,39(2):546-550. [44]LI H L,MENG Z Q.Attribute reduction algorithm using information gain and inconsistency to fill[J].Computer Science,2018,45(10):217-224. [45]XIE J Y,DING L J,WANG M Z.Spectral clustering based unsupervised feature selection algorithms[J].Journal of Software,2020,31(4):1009-1024. [46]SUN L,ZHANG J X,DING W P,et al.Feature reduction for imbalanced data classification using similarity-based feature clustering with adaptive weighted k-nearest neighbors[J].Information Sciences,2022,593:591-613. [47]SUN L,WANG T X,DING W P,et al.Two-stage-neighborhood-based multilabel classification for incomplete data with missing labels[J].International Journal of Intelligent Systems,2022,37:6773-6810. |
| [1] | 王泰彦, 潘祖烈, 于璐, 宋景彬. 基于预训练汇编指令表征的二进制代码相似性检测方法 Binary Code Similarity Detection Method Based on Pre-training Assembly Instruction Representation 计算机科学, 2023, 50(4): 288-297. https://doi.org/10.11896/jsjkx.220300271 |
| [2] | 陈奕君, 高浩然, 丁志军. 基于动态机器学习的信用评估模型 Credit Evaluation Model Based on Dynamic Machine Learning 计算机科学, 2023, 50(1): 59-68. https://doi.org/10.11896/jsjkx.220800191 |
| [3] | 胡安祥, 尹小康, 朱肖雅, 刘胜利. 基于数据流特征的比较类函数识别方法 Strcmp-like Function Identification Method Based on Data Flow Feature Matching 计算机科学, 2022, 49(9): 326-332. https://doi.org/10.11896/jsjkx.220200163 |
| [4] | 李斌, 万源. 基于相似度矩阵学习和矩阵校正的无监督多视角特征选择 Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment 计算机科学, 2022, 49(8): 86-96. https://doi.org/10.11896/jsjkx.210700124 |
| [5] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
| [6] | 康雁, 王海宁, 陶柳, 杨海潇, 杨学昆, 王飞, 李浩. 混合改进的花授粉算法与灰狼算法用于特征选择 Hybrid Improved Flower Pollination Algorithm and Gray Wolf Algorithm for Feature Selection 计算机科学, 2022, 49(6A): 125-132. https://doi.org/10.11896/jsjkx.210600135 |
| [7] | 储安琪, 丁志军. 基于灰狼优化算法的信用评估样本均衡化与特征选择同步处理 Application of Gray Wolf Optimization Algorithm on Synchronous Processing of Sample Equalization and Feature Selection in Credit Evaluation 计算机科学, 2022, 49(4): 134-139. https://doi.org/10.11896/jsjkx.210300075 |
| [8] | 孙林, 黄苗苗, 徐久成. 基于邻域粗糙集和Relief的弱标记特征选择方法 Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief 计算机科学, 2022, 49(4): 152-160. https://doi.org/10.11896/jsjkx.210300094 |
| [9] | 李宗然, 陈秀宏, 陆赟, 邵政毅. 鲁棒联合稀疏不相关回归 Robust Joint Sparse Uncorrelated Regression 计算机科学, 2022, 49(2): 191-197. https://doi.org/10.11896/jsjkx.210300034 |
| [10] | 吕小少, 舒辉, 康绯, 黄宇垚. 基于语义导向的软件在线升级功能逆向定位 Reverse Location of Software Online Upgrade Function Based on Semantic Guidance 计算机科学, 2022, 49(12): 353-361. https://doi.org/10.11896/jsjkx.211000059 |
| [11] | 王盼红, 朱昌明. MIF-CNNIF:一种基于CNN的交叉特征的多分类图像数据框架 MIF-CNNIF:A Multi-classification Image Data Framework Based on CNN with Intersect Features 计算机科学, 2022, 49(11A): 210800267-8. https://doi.org/10.11896/jsjkx.210800267 |
| [12] | 俞赛赛, 王小娟, 章倩倩. 基于启发式搜索特征选择的加密流量恶意行为检测技术 Detection of Malicious Behavior in Encrypted Traffic Based on Heuristic Search Feature Selection 计算机科学, 2022, 49(11A): 210800237-6. https://doi.org/10.11896/jsjkx.210800237 |
| [13] | 李永红, 汪盈, 李腊全, 赵志强. 一种改进的特征选择算法在邮件过滤中的应用 Application of Improved Feature Selection Algorithm in Spam Filtering 计算机科学, 2022, 49(11A): 211000028-5. https://doi.org/10.11896/jsjkx.211000028 |
| [14] | 闫振超, 舒文豪, 谢昕. 动态部分标记混合数据的增量式特征选择算法 Incremental Feature Selection Algorithm for Dynamic Partially Labeled Hybrid Data 计算机科学, 2022, 49(11): 98-108. https://doi.org/10.11896/jsjkx.210900076 |
| [15] | 张叶, 李志华, 王长杰. 基于核密度估计的轻量级物联网异常流量检测方法 Kernel Density Estimation-based Lightweight IoT Anomaly Traffic Detection Method 计算机科学, 2021, 48(9): 337-344. https://doi.org/10.11896/jsjkx.200600108 |
|
||
首页