Support vector machine (SVM) is an effective classification tool and maturely used in various fields. However, its performance is very sensitive to parameters. As a newly proposed swarm intelligence algorithm, snake optimizer algorithm (SO) can help to solve the parameter selection problem. Nevertheless, SO has the shortcomings of weak population initialization, slow convergence speed in the early stage, and being easy to fall into local optimization. To address these problems, an improved snake optimizer algorithm (ISO) was proposed. The mirror opposition-based learning mechanism (MOBL) improved the population quality to enhance the optimization speed. The novel evolutionary population dynamics model (NEPD) was beneficial for searching accurately. The differential evolution strategy (DES) helped to reduce the probability of falling into local optimal value. The experimental results of classical benchmark functions and CEC2022 showed that ISO had higher optimization precision and faster convergence rate. In addition, it was also applied to the parameter selection of SVM to demonstrate the effectiveness of the proposed ISO.
Citation: Hong Lu, Hongxiang Zhan, Tinghua Wang. A multi-strategy improved snake optimizer and its application to SVM parameter selection[J]. Mathematical Biosciences and Engineering, 2024, 21(10): 7297-7336. doi: 10.3934/mbe.2024322
Support vector machine (SVM) is an effective classification tool and maturely used in various fields. However, its performance is very sensitive to parameters. As a newly proposed swarm intelligence algorithm, snake optimizer algorithm (SO) can help to solve the parameter selection problem. Nevertheless, SO has the shortcomings of weak population initialization, slow convergence speed in the early stage, and being easy to fall into local optimization. To address these problems, an improved snake optimizer algorithm (ISO) was proposed. The mirror opposition-based learning mechanism (MOBL) improved the population quality to enhance the optimization speed. The novel evolutionary population dynamics model (NEPD) was beneficial for searching accurately. The differential evolution strategy (DES) helped to reduce the probability of falling into local optimal value. The experimental results of classical benchmark functions and CEC2022 showed that ISO had higher optimization precision and faster convergence rate. In addition, it was also applied to the parameter selection of SVM to demonstrate the effectiveness of the proposed ISO.
| [1] |
C. Cortes, V. N. Vapnik, Support-vector networks, Mach. Learn., 20 (1995), 273–297. https://doi.org/10.1007/BF00994018 doi: 10.1007/BF00994018
|
| [2] | M. V, D. T, M. Kalaiyarasi, Classification of newspaper article classification by employing support vector machine in comparison with perceptron to improve accuracy, in 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), (2023), 1–6. https://doi.org/10.1109/ICONSTEM56934.2023.10142309 |
| [3] | Q. Wang, R. Peng, J. Wang, Y. Xie, Y. Zhou, Research on text classification method of LDA-SVM based on PSO optimization, in 2019 Chinese Automation Congress (CAC), (2019), 1974–1978. https://doi.org/10.1109/CAC48633.2019.8996952 |
| [4] |
P. Preethi, H. R. Mamatha, Region-based convolutional neural network for segmenting text in epigraphical images, Artif. Intell. Appl., 1 (2022), 119–127, https://doi.org/10.47852/bonviewAIA2202293 doi: 10.47852/bonviewAIA2202293
|
| [5] | A. P. Baldovino, F. N. Vergonio, J. P. Tomas, Child attention detection through facial expression recognition using SVM algorithm, in Proceedings of the 2019 International Conference on Information Technology and Computer Communications, (2019), 52–58. https://doi.org/10.1145/3355402.3355411 |
| [6] |
H. Zhou, G. Yu, Research on pedestrian detection technology based on the SVM classifier trained by HOG and LTP features, Future Gener. Comp. Syst., 125 (2021), 604–615. https://doi.org/10.1016/j.future.2021.06.016 doi: 10.1016/j.future.2021.06.016
|
| [7] |
Y. Xu, Y. Li, C. K. Ahn, X. Chen, Seamless indoor pedestrian tracking by fusing INS and UWB measurements via LS-SVM assisted UFIR filter, Neurocomputing, 388 (2020), 301–308. https://doi.org/10.1016/j.neucom.2019.12.121 doi: 10.1016/j.neucom.2019.12.121
|
| [8] | J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT press, Cambridge, 1992. |
| [9] |
R. Storn, K. Price, Differential evolution–A simple and efficient adaptive scheme for global optimization over continuous spaces, J. Global Optim., 23 (1997), 341–359. https://doi.org/10.1023/A:1008202821328 doi: 10.1023/A:1008202821328
|
| [10] |
Z. Hu, X. Xu, Q. Su, H. Zhu, J. Guo, Grey prediction evolution algorithm for global optimization, Appl. Math. Model., 79 (2020), 145–160. https://doi.org/10.1016/j.apm.2019.10.026 doi: 10.1016/j.apm.2019.10.026
|
| [11] |
I. Segovia-Domínguez, R. Herrera-Guzmán, J. P. Serrano-Rubio, A. Hernández-Aguirre, Geometric probabilistic evolutionary algorithm, Expert Syst. Appl., 144 (2020), 113080. https://doi.org/10.1016/j.eswa.2019.113080 doi: 10.1016/j.eswa.2019.113080
|
| [12] | D. Bertsimas, J. Tsitsiklis, Simulated Annealing, Stat. Sci., 8 (1993), 10–15. |
| [13] |
S. Mirjalili, SCA: A sine cosine algorithm for solving optimization problems, Knowl.-Based Syst., 96 (2016), 120–133. https://doi.org/10.1016/j.knosys.2015.12.022 doi: 10.1016/j.knosys.2015.12.022
|
| [14] |
E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: A gravitational search algorithm, Inform. Sciences, 179 (2009), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004 doi: 10.1016/j.ins.2009.03.004
|
| [15] |
W. Zhao, L. Wang, Z. Zhang, Atom search optimization and its application to solve a hydrogeologic parameter estimation problem, Knowl.-Based Syst., 163 (2019), 283–304. https://doi.org/10.1016/j.knosys.2018.08.030 doi: 10.1016/j.knosys.2018.08.030
|
| [16] |
Anita, A. Yadav, AEFA: Artificial electric field algorithm for global optimization, Swarm Evol. Comput., 48 (2019), 93–108. https://doi.org/10.1016/j.swevo.2019.03.013 doi: 10.1016/j.swevo.2019.03.013
|
| [17] |
M. Cheng, M. N. Sholeh, Optical microscope algorithm: A new metaheuristic inspired by microscope magnification for solving engineering optimization problems, Knowl.-Based Syst., 279 (2023), 110939. https://doi.org/10.1016/j.knosys.2023.110939 doi: 10.1016/j.knosys.2023.110939
|
| [18] |
R. V. Rao, V. J. Savsani, D. P. Vakharia, Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems, Comput. Aided Design, 43 (2011), 303–315. https://doi.org/10.1016/j.cad.2010.12.015 doi: 10.1016/j.cad.2010.12.015
|
| [19] |
M. Kumar, A. J. Kulkarni, S. C. Satapathy, Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology, Future Gener. Comput. Syst., 81 (2018), 252–272. https://doi.org/10.1016/j.future.2017.10.052 doi: 10.1016/j.future.2017.10.052
|
| [20] |
P. Zhang, L. Wang, Z. Fei, L. Wei, M. Fei, M. I. Menhas, A novel human learning optimization algorithm with Bayesian inference learning, Knowl.-Based Syst., 271 (2023), 110564. https://doi.org/10.1016/j.knosys.2023.110564 doi: 10.1016/j.knosys.2023.110564
|
| [21] |
B. Das, V. Mukherjee, D. Das, Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems, Adv. Eng. Softw., 146 (2020), 102804. https://doi.org/10.1016/j.advengsoft.2020.102804 doi: 10.1016/j.advengsoft.2020.102804
|
| [22] |
S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Softw., 69 (2014), 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007 doi: 10.1016/j.advengsoft.2013.12.007
|
| [23] |
M. S. Kiran, TSA: Tree-seed algorithm for continuous optimization, Expert Syst. Appl., 42 (2015), 6686–6698. https://doi.org/10.1016/j.eswa.2015.04.055 doi: 10.1016/j.eswa.2015.04.055
|
| [24] |
S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Softw., 95 (2016), 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 doi: 10.1016/j.advengsoft.2016.01.008
|
| [25] |
A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future Gener. Comput. Syst., 97 (2019), 849–872. https://doi.org/10.1016/j.future.2019.02.028 doi: 10.1016/j.future.2019.02.028
|
| [26] |
N. Chopra, M. M. Ansari, Golden jackal optimization: A novel nature-inspired optimizer for engineering applications, Expert Syst. Appl., 198 (2022), 116924. https://doi.org/10.1016/j.eswa.2022.116924 doi: 10.1016/j.eswa.2022.116924
|
| [27] |
M. Dehghani, Š. Hubálovský , P. Trojovský , Northern goshawk optimization: A new swarm-based algorithm for solving optimization problems, IEEE Access, 9 (2021), 162059–162080. https://doi.org/10.1109/ACCESS.2021.3133286 doi: 10.1109/ACCESS.2021.3133286
|
| [28] |
C. Ma, H. Huang, Q. Fan, J. Wei, Y. Du, W. Gao, Grey wolf optimizer based on Aquila exploration method, Expert Syst. Appl., 205 (2022), 117629. https://doi.org/10.1016/j.eswa.2022.117629 doi: 10.1016/j.eswa.2022.117629
|
| [29] |
H. Kang, R. Liu, Y. Yao, F. Yu, Improved Harris hawks optimization for non-convex function optimization and design optimization problems, Math. Comput. Simulat., 204 (2023), 619–639. https://doi.org/10.1016/j.matcom.2022.09.010 doi: 10.1016/j.matcom.2022.09.010
|
| [30] |
T. Lou, Z. Yue, Y. Jiao, Z. He, A hybrid strategy-based GJO algorithm for robot path planning, Expert Syst. Appl., 238 (2024), 121975. https://doi.org/10.1016/j.eswa.2023.121975 doi: 10.1016/j.eswa.2023.121975
|
| [31] |
K. Li, H. Huang, S. Fu, C. Ma, Q. Fan, Y. Zhu, A multi-strategy enhanced northern goshawk optimization algorithm for global optimization and engineering design problems, Comput. Method. Appl. Mech. Eng., 415 (2023), 116199. https://doi.org/10.1016/j.cma.2023.116199 doi: 10.1016/j.cma.2023.116199
|
| [32] |
X. Lin, X. Yu, W. Li, A heuristic whale optimization algorithm with niching strategy for global multi-dimensional engineering optimization, Comput. Ind. Eng., 171 (2022), 108361. https://doi.org/10.1016/j.cie.2022.108361 doi: 10.1016/j.cie.2022.108361
|
| [33] |
F. A. Hashim, A. G. Hussien, Snake optimizer: A novel meta-heuristic optimization algorithm, Knowl.-Based Syst., 242 (2022), 108320. https://doi.org/10.1016/j.knosys.2022.108320 doi: 10.1016/j.knosys.2022.108320
|
| [34] |
H. Li, G. Xu, B. Chen, S. Huang, Y. Xia, S. Chai, Dual-mutation mechanism-driven snake optimizer for scheduling multiple budget constrained workflows in the cloud, Appl. Soft Comput., 149 (2023), 110966. https://doi.org/10.1016/j.asoc.2023.110966 doi: 10.1016/j.asoc.2023.110966
|
| [35] |
C. Wang, S. Jiao, Y. Li, Q. Zhang, Capacity optimization of a hybrid energy storage system considering wind-solar reliability evaluation based on a novel multi-strategy snake optimization algorithm, Expert Syst. Appl., 231 (2023), 120602. https://doi.org/10.1016/j.eswa.2023.120602 doi: 10.1016/j.eswa.2023.120602
|
| [36] |
R. A. Khurma, D. Albashish, M. Braik, A. Alzaqebah, A. Qasem, O. Adwan, An augmented snake optimizer for diseases and COVID-19 diagnosis, Biomed. Signal Proces., 84 (2023), 104718. https://doi.org/10.1016/j.bspc.2023.104718 doi: 10.1016/j.bspc.2023.104718
|
| [37] |
C. Yan, N. Razmjooy, Optimal lung cancer detection based on CNN optimized and improved snake optimization algorithm, Biomed. Signal Proces., 86 (2023), 105319. https://doi.org/10.1016/j.bspc.2023.105319 doi: 10.1016/j.bspc.2023.105319
|
| [38] |
E. H. Houssein, N. Abdalkarim, K. Hussain, E. Mohamed, Accurate multilevel thresholding image segmentation via oppositional snake optimization algorithm: Real cases with liver disease, Comput. Biol. Med., 169 (2024), 107922. https://doi.org/10.1016/j.compbiomed.2024.107922 doi: 10.1016/j.compbiomed.2024.107922
|
| [39] |
M. S. Braik, A. I. Hammouri, M. A. Awadallah, M. A. Al-Betar, O. A. Alzubi, Improved versions of snake optimizer for feature selection in medical diagnosis: a real case COVID-19, Soft Comput., 27 (2023), 17833–17865. https://doi.org/10.1007/s00500-023-09062-3 doi: 10.1007/s00500-023-09062-3
|
| [40] |
K. K. Mohammed, S. Mekhilef, Improved snake optimizer algorithm-based GMPPT with a fast response to the load variations under different weather conditions for PV systems, IEEE T. Ind. Electron., 71 (2024), 7147–7157. https://doi.org/10.1109/TIE.2023.3301526 doi: 10.1109/TIE.2023.3301526
|
| [41] |
Y. Li, B. Tang, S. Jiao, Q. Su, Snake optimization-based variable-step multiscale single threshold slope entropy for complexity analysis of signals, IEEE T. Instrum. Meas., 72 (2023), 1–13. https://doi.org/10.1109/TIM.2023.3317908 doi: 10.1109/TIM.2023.3317908
|
| [42] |
G. Hu, R. Yang, M. Abbas, G. Wei, BEESO: Multi-strategy boosted snake-inspired optimizer for engineering applications, J. Bionic Eng., 20 (2023), 1791–1827. https://doi.org/10.1007/s42235-022-00330-w doi: 10.1007/s42235-022-00330-w
|
| [43] |
L. Yao, P. Yuan, C. Tsai, T. Zhang, Y. Lu, S. Ding, ESO: An enhanced snake optimizer for real-world engineering problems, Expert Syst. Appl., 230 (2023), 120594. https://doi.org/10.1016/j.eswa.2023.120594 doi: 10.1016/j.eswa.2023.120594
|
| [44] | H. R. Tizhoosh, Opposition-based learning: A new scheme for machine intelligence, in International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), (2005), 695–701. https://doi.org/10.1109/CIMCA.2005.1631345 |
| [45] |
D. J. Kalita, V. P. Singh, V. Kumar, A dynamic framework for tuning SVM hyper parameters based on moth-flame optimization and knowledge-based-search, Expert Syst. Appl., 168 (2021), 114139. https://doi.org/10.1016/j.eswa.2020.114139 doi: 10.1016/j.eswa.2020.114139
|
| [46] |
Q. Huang, C. Wang, Y. Ye, L. Wang, N. Xie, Recognition of EEG based on improved black widow algorithm optimized SVM, Biomed. Signal Proces., 81 (2023), 104454. https://doi.org/10.1016/j.bspc.2022.104454 doi: 10.1016/j.bspc.2022.104454
|
| [47] |
Y. Qiu, J. Zhou, Short-term rockburst prediction in underground project: insights from an explainable and interpretable ensemble learning model, Acta Geotech., 18 (2023), 6655–6685. https://doi.org/10.1007/s11440-023-01988-0 doi: 10.1007/s11440-023-01988-0
|
| [48] |
Y. Qiu, J. Zhou, Short-Term rockburst damage assessment in burst-prone mines: An explainable XGBOOST hybrid model with SCSO algorithm, Rock Mech. Rock Eng., 56 (2023), 8745–8770. https://doi.org/10.1007/s00603-023-03522-w doi: 10.1007/s00603-023-03522-w
|
| [49] |
S. Cheng, J. Gao, H. Qi, Determination of the pile drivability using random forest optimized by particle swarm optimization and bayesian optimizer, Comput. Model. Eng., 141 (2024), 871–892. https://doi.org/10.32604/cmes.2024.052830 doi: 10.32604/cmes.2024.052830
|
| [50] |
Y. Song, L. Han, B. Zhang, W. Deng, A dual-time dual-population multi-objective evolutionary algorithm with application to the portfolio optimization problem, Eng. Appl. Artif. Intel., 133 (2024), 108638. https://doi.org/10.1016/j.engappai.2024.108638 doi: 10.1016/j.engappai.2024.108638
|
| [51] |
W Deng, X Cai, D Wu, Y Song, H Chen, X Ran, et al., MOQEA/D: multi-objective QEA with decomposition mechanism and excellent global search and its application, IEEE T. Intell. Transp. Syst., (2024). https://doi.org/10.1109/TITS.2024.3373510 doi: 10.1109/TITS.2024.3373510
|
| [52] | K. Bhosle, V. Musande, Evaluation of deep learning CNN model for recognition of devanagari digit, in Artificial intelligence and applications, (2023), 114–118. https://doi.org/10.47852/bonviewAIA3202441 |
| [53] | T. O. Akande, O. O. Alabi, S. A. Ajagbe, A deep learning-based CAE approach for simulating 3D vehicle wheels under real-world conditions, in Artificial Intelligence and Applications, (2024). https://doi.org/10.47852/bonviewAIA42021882 |
| [54] |
Q. Sun, J. Chen, L. Zhou, S. Ding, S. Han, A study on ice resistance prediction based on deep learning data generation method, Ocean Eng., 301 (2024), 117467. https://doi.org/10.1016/j.oceaneng.2024.117467 doi: 10.1016/j.oceaneng.2024.117467
|
Figures(5) / Tables(10)
Hong Lu, Hongxiang Zhan, Tinghua Wang. A multi-strategy improved snake optimizer and its application to SVM parameter selection[J]. Mathematical Biosciences and Engineering, 2024, 21(10): 7297-7336. doi: 10.3934/mbe.2024322
DownLoad: