计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 154-160.doi: 10.11896/jsjkx.190600068
张严, 秦亮曦
ZHANG Yan, QIN Liang-xi
摘要： 针对樽海鞘群算法(Salp Swarm Algorithm,SSA)在寻优过程中存在的收敛速度较慢、容易陷入局部最优的缺点,提出了一种改进的采用莱维飞行策略的条件化更新的樽海鞘群算法(Levy Flight-based Conditional Updating Salp Swarm Algorithm,LECUSSA),并将其运用于分类算法的特征子集选择过程。首先,利用莱维飞行策略的长短跳跃特点对领导者位置进行随机更新,以增强全局最优的搜索能力;其次,增加对追随者位置的更新条件,让追随者不再盲目地跟随,从而加快收敛速度。在23个优化基准函数上对LECUSSA算法与其他算法进行了性能比较实验;并把算法运用到支持向量机(Support Vector Machine,SVM)算法的分类特征子集选择中,采用8个UCI数据集对特征选择后的分类结果进行了性能比较实验。实验结果表明,LECUSSA具有良好的全局最优搜索能力和较快的收敛速度,利用LECUSSA算法进行特征选择后,能够找到最佳分类准确率的特征子集。
中图分类号:
| [1]WEBB B.Swarm Intelligence:From Natural to Artificial Systems[J].Connection Science,2002,14(2):163-164. [2]KENNEDY J,EBERHART R.Particle swarm optimization[C]//Proceedings of the IEEE International Conference on Neural Networks.1995:1942-1948. [3]YANG X S.Firefly algorithms for multimodal optimization[C]//International Symposium on Stochastic Algorithms.Berlin:Springer,2009:169-178. [4]YANG X S,DEB S.Cuckoo search via Lévy flights[C]//2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).IEEE Press,2009:210-214. [5]MIRJALILI S,MIRJALILI S M,LEWIS A.Grey Wolf Optimizer[J].Advances in Engineering Software,2014,69:46-61. [6]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(4):1053-1073. [7]MIRJALILI S,LEWIS A.The Whale Optimization Algorithm[J].Advances in Engineering Software,2016,95:51-67. [8]LIU H,MOTODA H.Feature selection for knowledge discovery and data mining[M].Springer Science & Business Media,2012:73-95. [9]MIRJALILI S,GANDOMI A H,MIRJALILI S Z,et al.Salp Swarm Algorithm:A bio-inspired optimizer for engineering design problems[J].Advances in Engineering Software,2017,114:163-191. [10]CHEN T,WANG M X,HUANG X S.Passive TDOA location based on Bohai Sheath Swarm Algorithms [J].Chinese Journal of Electronics and Information,2018,40(7):72-78. [11]HUSSIEN A G,HASSANIEN A E,HOUSSEIN E H.Swarming behaviour of salps algorithm for predicting chemical compound activities[C]//2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS).IEEE Press,2017:315-320. [12]WANG M Q,WANG Y,JI Z C.PMSM multi-parameter identification based on improved salp swarm algorithm [J].Chinese Journal of System Simulation,2018(11):4284-4291. [13]QAIS M H,HASANIEN H M,ALGHUWAINEM S.Enhanced salp swarm algorithm:Application to variable speed wind genera-tors[J].Engineering Applications of Artificial Intelligence,2019,80:82-96. [14]XING Z K,JIA H M,SONG W L.Multi-threshold image segmentation based on Levy’s Flying Bowl Sheath swarm optimization algorithm [J/OL].Chinese Journal of Automation. [2019-08-10].https://doi.org/10.16383/j.aas.c180140. [15]HEGAZY A E,MAKHLOUF M A,EL-TAWEL G S.Improved salp swarm algorithm for feature selection[J].Journal of King Saud University-Computer and Information Sciences,2020,32(3):355-344. [16]FARIS H,MAFARJA M M,HEIDARI A A,et al.An efficient binary salp swarm algorithm with crossover scheme for feature selection problems[J].Knowledge-Based Systems,2018,154:43-67. [17]IBRAHIM R A,EWEES A A,OLIVA D,et al.Improved salp swarm algorithm based on particle swarm optimization for feature selection[J].Journal of Ambient Intelligence andHuma-nized Computing,2019,10(8):3155-3169. [18]SAYED G I,KHORIBA G,HAGGAG M H.A novel chaotic salp swarm algorithm for global optimization and feature selection[J].Applied Intelligence,2018,48(10):3462-3481. [19]YAN X F,YE D Y.An improved flora foraging algorithm based on Levy flight [J].Computer system applications,2015,24 (3):124-132. [20]HÜSEYIN H,HARUN U.A novel particle swarm optimization algorithm with LeÜvy flight[J].Applied Soft Computing,2014,23:333-345. [21]AYDOĞDU Î,AKIN A,SAKA M P.Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution[J].Advances in Engineering Software,2016,92:1-14. [22]LIU C P,YE C M.Bat algorithm with Levy flight characteristics [J].Chinese Journal of Intelligent Systems,2013,8(3):240-246. [23]BIAGINI F.Stochastic calculus for fractional Brownian motion and applications[M].London:Springer,2008:5-20. [24]ZHU X E,HAO X,XIA S R.Levy flight-based feature selection algorithm [J].Chinese Journal of Zhejiang University (Engineering Edition),2013(4):638-643. [25]EDWARDS A M,PHILLIPS R A,WATKINS N W,et al.Revisiting Lévyflight search patterns of wandering albatrosses,bumblebees and deer [J].Nature,2007,449(7165):1044-1048. [26]EMBRECHTS P,KLÜPPELBERG C,MIKOSCH T.Modelling extremal events:for insurance and finance[M]//Science &Busi-ness Media.Springer,2013:371-403. [27]MANTEGNA R N.Fast,accurate algorithm for numerical simulation of Lévy stable stochastic processes[J].Physical Review.E,1994,49(5):4677-4689. |
| [1] | 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 |
| [2] | 陈俊, 何庆, 李守玉. 基于自适应反馈调节因子的阿基米德优化算法 Archimedes Optimization Algorithm Based on Adaptive Feedback Adjustment Factor 计算机科学, 2022, 49(8): 237-246. https://doi.org/10.11896/jsjkx.210700150 |
| [3] | 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 |
| [4] | 范星泽, 禹梅. 改进灰狼算法的无线传感器网络覆盖优化 Coverage Optimization of WSN Based on Improved Grey Wolf Optimizer 计算机科学, 2022, 49(6A): 628-631. https://doi.org/10.11896/jsjkx.210500037 |
| [5] | 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 |
| [6] | 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 |
| [7] | 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 |
| [8] | 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 |
| [9] | 罗文聪, 郑嘉利, 全艺璇, 谢孝德, 林子涵. 基于改进型多目标樽海鞘群算法的RFID阅读器天线优化部署 Optimized Deployment of RFID Reader Antenna Based on Improved Multi-objective Salp Swarm Algorithm 计算机科学, 2021, 48(9): 292-297. https://doi.org/10.11896/jsjkx.200700167 |
| [10] | 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 |
| [11] | 杨蕾, 降爱莲, 强彦. 基于自编码器和流形正则的结构保持无监督特征选择 Structure Preserving Unsupervised Feature Selection Based on Autoencoder and Manifold Regularization 计算机科学, 2021, 48(8): 53-59. https://doi.org/10.11896/jsjkx.200700211 |
| [12] | 侯春萍, 赵春月, 王致芃. 基于自反馈最优子类挖掘的视频异常检测算法 Video Abnormal Event Detection Algorithm Based on Self-feedback Optimal Subclass Mining 计算机科学, 2021, 48(7): 199-205. https://doi.org/10.11896/jsjkx.200800146 |
| [13] | 胡艳梅, 杨波, 多滨. 基于网络结构的正则化逻辑回归 Logistic Regression with Regularization Based on Network Structure 计算机科学, 2021, 48(7): 281-291. https://doi.org/10.11896/jsjkx.201100106 |
| [14] | 周钢, 郭福亮. 基于特征选择的高维数据集成学习方法研究 Research on Ensemble Learning Method Based on Feature Selection for High-dimensional Data 计算机科学, 2021, 48(6A): 250-254. https://doi.org/10.11896/jsjkx.200700102 |
| [15] | 章菊, 李学鋆. 基于莱维萤火虫算法的智能生产线调度问题研究 Research on Intelligent Production Line Scheduling Problem Based on LGSO Algorithm 计算机科学, 2021, 48(6A): 668-672. https://doi.org/10.11896/jsjkx.210300118 |
|
||
首页