Research article

An improved dung beetle optimizer based on Padé approximation strategy for global optimization and feature selection

  • Feature selection is a crucial data processing method used to reduce dataset dimensionality while preserving key information. In this paper, we proposed a multi-strategy enhanced dung beetle optimization algorithm (mDBO) that integrates multiple strategies to effectively address the feature selection problem. First, a novel population initialization strategy based on a hybrid tent-sine map and random opposition-based learning was proposed to generate initial population. This strategy yielded a more uniform distribution of the initial population, significantly improving the quality of the population distribution within the search space. Second, a new differential evolution mutation strategy with a periodic retrospective adaptive mutation factor was proposed. This strategy effectively improved the algorithm's ability to jump out of the local optimal and explore potential candidate solutions. Third, based on Padé approximation technology and the novel adaptive evolutionary boundary constraint method, an innovative approximation strategy was proposed. The strategy was integrated into the framework of the dung beetle optimizer, significantly improving the solution accuracy and population quality of the algorithm. Finally, the binary version of the mDBO algorithm (bmDBO) was applied to feature selection tasks. Experiments entailing CEC2017 benchmark functions and 17 datasets showed that both mDBO and bmDBO outperformed other algorithms. The mDBO method outperformed other algorithms in 11 of the 29 benchmark functions, ranked second in 8 functions, and achieved an average rank of 1.62 in the Friedman ranking, securing the overall first place; the bmDBO method outperformed in 12 of 17 datasets, achieving an average ranking of 1.35 in the Friedman ranking, securing the first position.

    Citation: Tianbao Liu, Lingling Yang, Yue Li, Xiwen Qin. An improved dung beetle optimizer based on Padé approximation strategy for global optimization and feature selection[J]. Electronic Research Archive, 2025, 33(3): 1693-1762. doi: 10.3934/era.2025079

    Related Papers:

    [1] Kochar Khasro Saleh, Semih Dalkiliç, Lütfiye Kadioğlu Dalkiliç, Bahra Radhaa Hamarashid, Sevda Kirbağ . Targeting cancer cells: from historic methods to modern chimeric antigen receptor (CAR) T-Cell strategies. AIMS Allergy and Immunology, 2020, 4(2): 32-49. doi: 10.3934/Allergy.2020004
    [2] Issam Tout, Marie Marotel, Isabelle Chemin, Uzma Hasan . HBV and the importance of TLR9 on B cell responses. AIMS Allergy and Immunology, 2017, 1(3): 124-137. doi: 10.3934/Allergy.2017.3.124
    [3] Andrey Mamontov, Alexander Polevshchikov, Yulia Desheva . Mast cells in severe respiratory virus infections: insights for treatment and vaccine administration. AIMS Allergy and Immunology, 2023, 7(1): 1-23. doi: 10.3934/Allergy.2023001
    [4] Ling Wang, Shunbin Ning . “Toll-free” pathways for production of type I interferons. AIMS Allergy and Immunology, 2017, 1(3): 143-163. doi: 10.3934/Allergy.2017.3.143
    [5] Daniil Shevyrev, Valeriy Tereshchenko, Olesya Manova, Vladimir kozlov . Homeostatic proliferation as a physiological process and a risk factor for autoimmune pathology. AIMS Allergy and Immunology, 2021, 5(1): 18-32. doi: 10.3934/Allergy.2021002
    [6] Stefano Regis, Fabio Caliendo, Alessandra Dondero, Francesca Bellora, Beatrice Casu, Cristina Bottino, Roberta Castriconi . Main NK cell receptors and their ligands: regulation by microRNAs. AIMS Allergy and Immunology, 2018, 2(2): 98-112. doi: 10.3934/Allergy.2018.2.98
    [7] Ken S. Rosenthal, Daniel H. Zimmerman . J-LEAPS vaccines elicit antigen specific Th1 responses by promoting maturation of type 1 dendritic cells (DC1). AIMS Allergy and Immunology, 2017, 1(2): 89-100. doi: 10.3934/Allergy.2017.2.89
    [8] James Peterson . Affinity and avidity models in autoimmune disease. AIMS Allergy and Immunology, 2018, 2(1): 45-81. doi: 10.3934/Allergy.2018.1.45
    [9] Michael D. Caponegro, Jeremy Tetsuo Miyauchi, Stella E. Tsirka . Contributions of immune cell populations in the maintenance, progression, and therapeutic modalities of glioma. AIMS Allergy and Immunology, 2018, 2(1): 24-44. doi: 10.3934/Allergy.2018.1.24
    [10] Caterina Marangio, Rosa Molfetta, Erisa Putro, Alessia Carnevale, Rossella Paolini . Exploring the dynamic of NKG2D/NKG2DL axis: A central regulator of NK cell functions. AIMS Allergy and Immunology, 2025, 9(2): 70-88. doi: 10.3934/Allergy.2025005
  • Feature selection is a crucial data processing method used to reduce dataset dimensionality while preserving key information. In this paper, we proposed a multi-strategy enhanced dung beetle optimization algorithm (mDBO) that integrates multiple strategies to effectively address the feature selection problem. First, a novel population initialization strategy based on a hybrid tent-sine map and random opposition-based learning was proposed to generate initial population. This strategy yielded a more uniform distribution of the initial population, significantly improving the quality of the population distribution within the search space. Second, a new differential evolution mutation strategy with a periodic retrospective adaptive mutation factor was proposed. This strategy effectively improved the algorithm's ability to jump out of the local optimal and explore potential candidate solutions. Third, based on Padé approximation technology and the novel adaptive evolutionary boundary constraint method, an innovative approximation strategy was proposed. The strategy was integrated into the framework of the dung beetle optimizer, significantly improving the solution accuracy and population quality of the algorithm. Finally, the binary version of the mDBO algorithm (bmDBO) was applied to feature selection tasks. Experiments entailing CEC2017 benchmark functions and 17 datasets showed that both mDBO and bmDBO outperformed other algorithms. The mDBO method outperformed other algorithms in 11 of the 29 benchmark functions, ranked second in 8 functions, and achieved an average rank of 1.62 in the Friedman ranking, securing the overall first place; the bmDBO method outperformed in 12 of 17 datasets, achieving an average ranking of 1.35 in the Friedman ranking, securing the first position.



    1. The Adoptive Cellular Therapy

    We are witnessing a rapid advancement in immunotherapy, in particular adoptive T-cell immunotherapy, against specific cancers over the past decade. Currently, adoptive T-cell immunotherapy comprises of three different classes through the use of tumor-infiltrating lymphocytes (TILs), chimeric antigen receptor (CAR)-and T-cell receptor (TCR)-engineered T cells [1]. While TILs are obtained through the isolation from tumor mass, the latter two methods obtain T cells through genetic engineering. Both CAR-and TCR-redirected systems result in the form of antigen-specific T cells, which would permit the immune system to confer an adequate anti-tumor immune response that maybe not present naturally [1].

    Several published articles on the distinction between TCR-and CAR-engineered T-cell systems are available (please read review [2] in particular). TCR is an αβ heterodimer receptor, naturally expressed on the T-cell surface, which binds to a specific peptide-major histocompatibility complex/MHC unit. TCR associates with CD3 molecules (γ, δ, ε and ζ chains) to provide intracellular signaling domains, which is a prerequisite for a T cell to confer an immune response. In addition, the presence of either co-receptor CD4 or CD8 supports the responsiveness of a T cell to be activated by TCR binding to as few as one peptide-MHC unit. This sensitive, yet specific system allows T cells to physiologically target intracellular antigens in a form of peptide-MHC complexes [2]. The CAR refers to a synthetic construct typically comprising a single-chain antibody variable fragment, an extracellular domain/hinge, a transmembrane domain, one or more intracellular signaling domains (e.g. CD28 or 4-1BB) and cytoplasmic immunoreceptor tyrosine activation motifs derived from CD3-ζ chain [3]. Less commonly, the CD3-ζ chain is substituted with the γ chain of FcεRI or the CD3-ε chain [4]. The CAR system can target cell surface antigens independent of MHC. Transformed cells, such as cancerous cells, usually express a particular cell surface antigen at high density, hence these kinds of target cells can be recognized and eliminated by the CAR-engineered T cells [2]. Due to the pronounced differences between the CAR and TCR systems (as shown in Figure 1), it is indeed difficult to directly compare these two systems. Nonetheless, it is fair to state that CAR system has more potential to be utilized in a wide population, since it does not face any MHC restriction. On the other hand, TCR is a more specific system than CAR due to its capability to recognize and respond to as few as only one particular peptide-MHC complex [2].

    Figure 1. Distinction between T-cell receptor and chimeric antigen receptor. (A & B) T-cell receptor/TCR is an αβ heterodimer that binds to a peptide presented by major histocompatibility complex/MHC. TCR is expressed as a functional complex along with CD3 molecules (γ, δ, ε and ζ chains). CD4 or CD8 molecule, as a co-receptor, supports the effective binding of a TCR complex to a particular peptide-MHC unit, i.e. CD4 recognizes MHC class Ⅱ, while CD8 recognizes MHC class Ⅰ. Furthermore, both CD4 and CD8 interact and bind lymphocyte-specific protein kinase/Lck. (C) Chimeric antigen receptor/CAR is a hybrid molecule contains a single-chain variable fragment (scFv) recognition domain, a hinge region, a transmembrane domain, a co-stimulatory molecule (typically CD28 or 4-1BB) and cytoplasmic immunoreceptor tyrosine-based activation motifs (not shown) derived from CD3-ζ chain. The scFV domain is derived from the heavy-and light-chain variable regions of a specific monoclonal antibody (VH and VL, respectively), hence it is capable to bind particular cell surface antigens (e.g. CD19). Both the TCR and CAR signalling are initiated by Lck-mediated phosphorylation of immuno-tyrosine activation motifs within the cytoplasmic domains of CD3 molecules.

    As mentioned above, both CAR and TCR systems are currently being explored as potential treatment targets in different cancers. While the clinical efficacy of CAR-engineered T cells against solid tumors is still limited, partly due to the local immunosuppressive tumor environment, it has been demonstrated that CAR treatment against CD19 antigen (expressed by B cells) resulted in complete remission in several patients suffering from B-cell malignancies [5,6,7,8]. Despite its efficacy, CAR therapy, however, is associated with several side effects. In general, it could cause "the cytokine release syndrome" because of the systemic release of pro-inflammatory cytokines, e.g. TNF-α and IL-6, at high levels [9]. In particular, CD19-specific CAR therapy has been reported to result in B-cell aplasia that prompts for exogenous administration of immunoglobulin [2]. On the other hand, trials using TCR-engineered T cells demonstrated some success in treating both solid and hematological tumors [2]. However, current TCR-engineered T cells have been developed to target tumor-associated antigens (e.g. MAGE-A3 or NY-ESO-1), which are actually self antigens perse [10]. This implies that TCR-generated T cells could cross-react to similar peptide-MHC complexes in heathy tissue, although are presented at very low levels, resulting in severe, sometime lethal adverse events [11,12]. Taken together, despite these two systems are potentially efficacious in treating certain cancers, their safety profiles are still of concern and they need to be addressed and properly rectified before they can be routinely used in the clinical setting.

    In addition, due to the complex process to produce a specific type of CAR-or TCR-engineered T cells per patient, there is a cautious remark questioning the ability of patients or public health system to pay substantial costs incurred by these modes of treatment. It is forecasted that the price ranges of CAR-or TCR-engineered T cells are between $120,000 and $300,000 per patient [13,14]. This estimated price seriously challenges the likelihood to implement these modes of treatment in the clinical setting [13]. Therefore, any creative solution to reduce the incurred costs, e.g. converting the engineered T cells from a specific patient-customized item to a mass-produced item [13], will be crucial in order to support and sustain the economic viability of these novel therapeutic modes.


    2. The Adoptive T Cells Against Viral Hepatitis

    Functional T-cell responses are required to control HBV and HCV replication, as well as to eliminate viral infection [15,16]. Therefore, both CAR and TCR systems have been studied the context of HBV and HCV infection as well. The published viral epitopes, targeted by CAR and TCR-engineered T cells, are summarized in Table 1. With respect to HCV infection, both systems have been extensively studied in vitro, i.e. by targeting HLA-A*02:01-restricted epitopes within HCV NS3 or NS5A protein, as well as HCV E2 glycoprotein in the TCR and CAR systems, respectively [17,18,19,20]. Both systems demonstrated their efficacy in controlling HCV replication in vitro with a low level of cytotoxicity. Nonetheless, subsequent in vivo and clinical studies are required to confirm whether these efficacies observed in vitro can be replicated without a profound risk of morbidity or mortality. Another hindrance is that potent antiviral drugs to cure HCV-infected patients are already available on the market [21], hence questioning the necessity to develop adoptive T-cell immunotherapy against chronic HCV infection.

    Table 1. Published HBV and HCV epitopes targeted by CAR-or TCR-engineered T cells.
    System HBV HCV
    CAR S domain of HBs antigen [22] e137 of HCV/E2 glycoprotein [18]
    TCR HLA-A*02:01-restricted HBs183-191 [14,24] HLA-A*02:01-restricted NS31073-1081 [17,19,20]
    HLA-A*02:01-restricted HBs370-379 [23,24] HLA-A*02:01-restricted NS5A1992-2000 [17,20]
    HLA-A*02:01-restricted HBc18-27 [23,24]
    HLA-C*08:01-restricted HBs171-80 [24]
     | Show Table
    DownLoad: CSV

    Lack of potent anti-HBV drugs in contrast, has accelerated studies on adoptive T-cell immunotherapy. A research group led by Ulrike Protzer uses CAR that targets HBV envelope protein and has tested this system in a HBV-transgenic mouse model. This group demonstrated that CAR-engineered T cells were able to control HBV replication with a transient liver damage in vivo. In addition, their study showed that the presence of HBs antigens in murine sera did not interfere with the functionality of HBV-specific CAR-engineered T cells [22]. However, it is noteworthy that the levels of HBs antigen in murine sera (~1,000–1,200 IU/mL) correspond only to the levels observed in the low-replicate phase of chronic Hepatitis B [22]. This implies that it is elusive yet on whether CAR-engineered T cells can be functional in patients with higher levels of HBs antigen. As an alternative, the HBV-specific TCR-engineered T-cell research, led primarily by Antonio Bertoletti's group, is focusing on targeting certain MHC-restricted epitopes within viral antigens, e.g. HBV surface or core antigen. This group has demonstrated that TCR-engineered T cells are also able to control HBV replication in both cell lines and xenograft mice [14,23].

    Taken together, both CAR-and TCR-engineered T cells have merits to be further developed as a potential treatment tool against chronic HBV infection. However, both methods are associated with a risk of significant liver inflammation and consequently, liver damage. Therefore, extensive studies are required to provide sufficient evidence in order to support the efficacy and safety of these treatment modes for patients with chronic HBV infection.


    3. The Adoptive T Cells Against Viral Hepatitis-associated Liver Cancer

    It has been acknowledged that chronic viral hepatitis contributes to the majority of primary hepatocellular carcinoma/HCC cases [25]. In line with the primary usage of adoptive T-cell therapy against cancers, this mode of treatment has a potential utility for the treatment of HBV-or HCV-associated HCC.

    It is important to point out that a high frequency of HBV DNA integration is observed in the genome of HBV-associated HCC cells, resulting in the expression of HBV antigens by tumor cells [26]. This allows the usage of TCR-engineered T cells to treat HBV-associated HCC. Unlike self antigens, HBV antigens are not found in healthy tissues. Hence, these viral antigens theoretically can serve as better target antigens in certain HCC cases. However, since non-cancerous but HBV-infected hepatocytes also express HBV antigens, HBV-specific TCR-engineered T cells could attack those infected hepatocytes. This potentially could cause severe liver damage.

    To address this concern, Bertoletti's group decided to treat a liver-transplanted patient who developed extrahepatic HCC metastasis with HBV-DNA integration, as the first use of HBV-specific TCR-engineered T cells in a clinical setting [27]. The patient importantly exhibited HBV surface antigen restricted by HLA-A*02:01 (i.e., HBs183-191 epitope), only in tissues with the extrahepatic HCC metastasis. Hence, the metastatic tissue could be attacked by HBV-specific TCR-engineered T cells with a significant reduced risk of damaging healthy liver tissue. Indeed, this group demonstrated the clinical potential and safety of using HBV-specific TCR-engineered T cells by choosing a suitable patient [27]. This finding still needs to be validated in clinical studies using large number of patients. Nonetheless, this milestone study suggests that the adoptive T-cell therapy can be used against a selected group of HBV-associated HCC cases, such as to prevent or treat HCC recurrence in liver-transplanted patients with HBV-positive HCC [28]. In addition, this research group recently modified its TCR engineering technology from the viral vector-based to mRNA electroporation-based method [14]. This TCR mRNA electroporation method is, arguably, a promising technology [14] due to its successful rate of engineering T cells (approximately half of the engineered T cells were endowed with antigen-specific functionality), its better safety profile (because of the transient functionality of engineered T cells) and its much reduced costs (approximately $30,000 per patient because of the less complexity and effort to engineer T cells). This improved technology, if proven, would sustain the economic viability of developing and implementing HBV-specific TCR-engineered T cells for clinical use.

    In contrast, HCV as an RNA virus does not integrate with the host genome. Therefore, despite a study demonstrated the utilization of HCV-specific TCR-engineered T cells against HCV-associated HCC in cell lines and xenograft mice [29], it will be difficult to select a suitable group of HCV-associated HCC patients in order to be treated with this treatment mode.


    4. Concluding Remark

    We are entering a new exciting era where adoptive T-cell immunotherapy is extensively studied against viral hepatitis-associated liver cancer. Based on the evidence presented in this review, we are optimistic and feel that the adoptive T-cell immunotherapy, at least in a form of TCR-engineered T cells, could serve as a novel alternative yet effective treatment for a selected group of HBV-associated HCC patients.


    Acknowledgment

    No fund or grant was received for this article.


    Conflict of Interest

    Both authors are also employees of Nutricia Research and therefore declare potential conflicts of interest.




    [1] M. Azizi, U. Aickelin, H. A. Khorshidi, M. Baghalzadeh-Shishehgarkhaneh, Energy valley optimizer: A novel metaheuristic algorithm for global and engineering optimization, Sci. Rep., 13 (2023), 226. https://doi.org/10.1038/s41598-022-27344-y doi: 10.1038/s41598-022-27344-y
    [2] F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, W. Al-Atabany, Honey badger algorithm: New metaheuristic algorithm for solving optimization problems, Math. Comput. Simul., 192 (2022), 84–110. https://doi.org/10.1016/j.matcom.2021.08.013 doi: 10.1016/j.matcom.2021.08.013
    [3] S. Gupta, H. Abderazek, B. S. Yıldız, A. R. Yildiz, S. Mirjalili, S. M. Sait, Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems, Exp. Syst. Appl., 183 (2021), 115351. https://doi.org/10.1016/j.eswa.2021.115351 doi: 10.1016/j.eswa.2021.115351
    [4] S. Aslan, T. Erkin, An immune plasma algorithm based approach for ucav path planning, J. King Saud Univ. Comput. Inf. Sci., 35 (2023), 56–69. https://doi.org/10.1016/j.jksuci.2022.06.004 doi: 10.1016/j.jksuci.2022.06.004
    [5] L. Abualigah, K. H. Almotairi, M. A. Elaziz, Multilevel thresholding image segmentation using meta-heuristic optimization algorithms: Comparative analysis, open challenges and new trends, Appl. Intell., 53 (2023), 11654–11704. https://doi.org/10.1007/s10489-022-04064-4 doi: 10.1007/s10489-022-04064-4
    [6] M. Chan-Ley, G. Olague, Categorization of digitized artworks by media with brain programming, Appl. Opt., 59 (2020), 4437–4447, 2020. https://doi.org/10.1364/AO.385552 doi: 10.1364/AO.385552
    [7] Y. Li, G. Tian, Y. Yi, Y. Yuan, Improved artificial rabbit optimization and its application in multichannel signal denoising, IEEE Sensors J., 2024 (2024). https://doi.org/10.1109/JSEN.2024.3456290 doi: 10.1109/JSEN.2024.3456290
    [8] Y. Xiao, H. Cui, A. G. Hussien, F. A. Hashim, MSAO: A multi-strategy boosted snow ablation optimizer for global optimization and real-world engineering applications, Adv. Eng. Inf., 61 (2024), 102464. https://doi.org/10.1016/j.aei.2024.102464 doi: 10.1016/j.aei.2024.102464
    [9] X. Fei, J. Wang, S. Ying, Z. Hu, J. Shi, Projective parameter transfer based sparse multiple empirical kernel learning machine for diagnosis of brain disease, Neurocomputing, 413 (2020), 271–283. https://doi.org/10.1016/j.neucom.2020.07.008 doi: 10.1016/j.neucom.2020.07.008
    [10] Z. Ma, X. Li, An improved supervised and attention mechanism-based u-net algorithm for retinal vessel segmentation, Comput. Biol. Med., 168 (2024), 107770. https://doi.org/10.1016/j.compbiomed.2023.107770 doi: 10.1016/j.compbiomed.2023.107770
    [11] Y. Xiao, Y. Guo, H. Cui, Y. Wang, J. Li, Y. Zhang, IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems, Math. Biosci. Eng., 19 (2022), 10963–11017. https://doi.org/10.3934/mbe.2022512 doi: 10.3934/mbe.2022512
    [12] J. Zhang, M. Xiao, L. Gao, Q. Pan, Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems, Appl. Math. Modell., 63 (2018), 464–490. https://doi.org/10.1016/j.apm.2018.06.036 doi: 10.1016/j.apm.2018.06.036
    [13] U. Kamath, K. De Jong, A. Shehu, Effective automated feature construction and selection for classification of biological sequences, PloS One, 9 (2014), e99982. https://doi.org/10.1371/journal.pone.0099982 doi: 10.1371/journal.pone.0099982
    [14] F. Thabtah, F. Kamalov, S. Hammoud, S. R. Shahamiri, Least loss: A simplified filter method for feature selection, Inf. Sci., 534 (2020), 1–15. https://doi.org/10.1016/j.ins.2020.05.017 doi: 10.1016/j.ins.2020.05.017
    [15] L. Sun, J. Zhang, W. Ding, J. Xu, Feature reduction for imbalanced data classification using similarity-based feature clustering with adaptive weighted k-nearest neighbors, Inf. Sci., 593 (2022), 591–613. https://doi.org/10.1016/j.ins.2022.02.004 doi: 10.1016/j.ins.2022.02.004
    [16] Z. Tao, L. Huiling, W. Wenwen, Y. Xia, GA-SVM based feature selection and parameter optimization in hospitalization expense modeling, Appl. Soft Comput., 75 (2019), 323–332. https://doi.org/10.1016/j.asoc.2018.11.001 doi: 10.1016/j.asoc.2018.11.001
    [17] H. Liu, Z. Zhao, Manipulating data and dimension reduction methods: Feature selection, in Computational Complexity: Theory, Techniques, and Applications, Springer, (2012), 790–1800. https://doi.org/10.1007/978-1-4614-1800-9_115
    [18] M. Rostami, K. Berahmand, E. Nasiri, S. Forouzandeh, Review of swarm intelligence-based feature selection methods, Eng. Appl. Artif. Intell., 100 (2021), 104210. https://doi.org/10.1016/j.engappai.2021.104210 doi: 10.1016/j.engappai.2021.104210
    [19] D. H. Wolpert, W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1 (1997), 67–82. https://doi.org/10.1109/4235.585893 doi: 10.1109/4235.585893
    [20] X. Song, Y. Zhang, D. Gong, X. Sun, Feature selection using bare-bones particle swarm optimization with mutual information, Pattern Recognit., 112 (2021), 107804. https://doi.org/10.1016/j.patcog.2020.107804 doi: 10.1016/j.patcog.2020.107804
    [21] H. Faris, M. M. Mafarja, A. A. Heidari, I. Aljarah, A. M. Al-Zoubi, S. Mirjalili, et al., An efficient binary salp swarm algorithm with crossover scheme for feature selection problems, Knowl. Based Syst., 154 (2018), 43–67. https://doi.org/10.1016/j.knosys.2018.05.009 doi: 10.1016/j.knosys.2018.05.009
    [22] E. Emary, H. M. Zawbaa, A. E. Hassanien, Binary grey wolf optimization approaches for feature selection, Neurocomputing, 172 (2016), 371–381. https://doi.org/10.1016/j.neucom.2015.06.083 doi: 10.1016/j.neucom.2015.06.083
    [23] Y. Zhao, J. Dong, X. Li, H. Chen, S. Li, A binary dandelion algorithm using seeding and chaos population strategies for feature selection, Appl. Soft Comput., 125 (2022), 109166. https://doi.org/10.1016/j.asoc.2022.109166 doi: 10.1016/j.asoc.2022.109166
    [24] M. Mafarja, S. Mirjalili, Whale optimization approaches for wrapper feature selection, Appl. Soft Comput., 62 (2018), 441–453. https://doi.org/10.1016/j.asoc.2017.11.006 doi: 10.1016/j.asoc.2017.11.006
    [25] A. Adamu, M. Abdullahi, S. B. Junaidu, I. H. Hassan, An hybrid particle swarm optimization with crow search algorithm for feature selection, Mach. Learn. Appl., 6 (2021), 100108. https://doi.org/10.1016/j.mlwa.2021.100108 doi: 10.1016/j.mlwa.2021.100108
    [26] Y. Xue, T. Tang, A. X. Liu, Large-scale feedforward neural network optimization by a self-adaptive strategy and parameter based particle swarm optimization, IEEE Access, 7 (2019), 52473–52483. https://doi.org/10.1109/ACCESS.2019.2911530 doi: 10.1109/ACCESS.2019.2911530
    [27] E. Aličković, A. Subasi, Breast cancer diagnosis using GA feature selection and rotation forest, Neural Comput. Appl., 28 (2017), 753–763. https://doi.org/10.1007/s00521-015-2103-9 doi: 10.1007/s00521-015-2103-9
    [28] B. Xue, M. Zhang, W. N. Browne, X. Yao, A survey on evolutionary computation approaches to feature selection, IEEE Trans. Evol. Comput., 20 (2015), 606–626. https://doi.org/10.1109/TEVC.2015.2504420 doi: 10.1109/TEVC.2015.2504420
    [29] N. Khodadadi, E. Khodadadi, Q. Al-Tashi, E. S. M. El-Kenawy, L. Abualigah, S. J. Abdulkadir, et al., BAOA: Binary arithmetic optimization algorithm with K-nearest neighbor classifier for feature selection, IEEE Access, 11 (2023), 94094–94115. https://doi.org/10.1109/ACCESS.2023.3310429 doi: 10.1109/ACCESS.2023.3310429
    [30] W. N. Chen, D. Z. Tan, Q. Yang, T. Gu, J. Zhang, Ant colony optimization for the control of pollutant spreading on social networks, IEEE Trans. Cybern., 50 (2019), 4053–4065. https://doi.org/10.1109/TCYB.2019.2922266 doi: 10.1109/TCYB.2019.2922266
    [31] H. Hichem, M. Elkamel, M. Rafik, M. T. Mesaaoud, C. Ouahiba, A new binary grasshopper optimization algorithm for feature selection problem, J. King Saud University Comput. Inf. Sci., 34 (2022), 316–328. https://doi.org/10.1016/j.jksuci.2019.11.007 doi: 10.1016/j.jksuci.2019.11.007
    [32] A. A. Alhussan, A. A. Abdelhamid, E. S. M. El-Kenawy, A. Ibrahim, M. M. Eid, D. S. Khafaga, et al., A binary waterwheel plant optimization algorithm for feature selection, IEEE Access, 11 (2023), 94227–94251. https://doi.org/10.1109/ACCESS.2023.3312022 doi: 10.1109/ACCESS.2023.3312022
    [33] K. Nag, N. R. Pal, A multiobjective genetic programming-based ensemble for simultaneous feature selection and classification, IEEE Trans. Cybern., 46 (2015), 499–510. https://doi.org/10.1109/TCYB.2015.2404806 doi: 10.1109/TCYB.2015.2404806
    [34] J. Xue, B. Shen, Dung beetle optimizer: A new meta-heuristic algorithm for global optimization, J. Supercomput., 79 (2023), 7305–7336. https://doi.org/10.1007/s11227-022-04959-6 doi: 10.1007/s11227-022-04959-6
    [35] X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster, IEEE Trans. Evol. Comput., 3 (1999), 82–102. https://doi.org/10.1109/4235.771163 doi: 10.1109/4235.771163
    [36] T. Y. Wu, H. Li, S. C. Chu, CPPE: An improved phasmatodea population evolution algorithm with chaotic maps, Mathematics, 11 (2023), 1977. https://doi.org/10.3390/math11091977 doi: 10.3390/math11091977
    [37] P. Qu, Q. Yuan, F. Du, Q. Gao, An improved manta ray foraging optimization algorithm, Sci. Rep., 14 (2024), 10301. https://doi.org/10.1038/s41598-024-59960-1 doi: 10.1038/s41598-024-59960-1
    [38] H. Yu, Y. Wang, H. Jia, L. Abualigah, Modified prairie dog optimization algorithm for global optimization and constrained engineering problems, Math. Biosci. Eng, 20 (2023), 19086–19132. https://doi.org/10.3934/mbe.2023844 doi: 10.3934/mbe.2023844
    [39] C. Liu, L. Li, Y. Qiang, S. Zhang, Predicting construction accidents on sites: An improved atomic search optimization algorithm approach, Eng. Rep., 6 (2024), e12773. https://doi.org/10.1002/eng2.12773 doi: 10.1002/eng2.12773
    [40] A. Bisht, M. Dua, S. Dua, A novel approach to encrypt multiple images using multiple chaotic maps and chaotic discrete fractional random transform, J. Ambient Intell. Humanized Comput., 10 (2019), 3519–3531. https://doi.org/10.1007/s12652-018-1072-0 doi: 10.1007/s12652-018-1072-0
    [41] Y. Zhou, L. Bao, C. L. P. Chen, A new 1D chaotic system for image encryption, Signal Process., 97 (2014), 172–182. https://doi.org/10.1016/j.sigpro.2013.10.034 doi: 10.1016/j.sigpro.2013.10.034
    [42] F. Han, X. Liao, B. Yang, Y. Zhang, A hybrid scheme for self-adaptive double color-image encryption, Multimedia Tools Appl., 77 (2018), 14285–14304. https://doi.org/10.1007/s11042-017-5029-7 doi: 10.1007/s11042-017-5029-7
    [43] 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), 1 (2005), 695–701. https://doi.org/10.1109/CIMCA.2005.1631345
    [44] S. Rahnamayan, H. R. Tizhoosh, M. M. A. Salama, Opposition-based differential evolution, IEEE Trans. Evol. Comput., 12 (2008), 64–79. https://doi.org/10.1109/TEVC.2007.894200 doi: 10.1109/TEVC.2007.894200
    [45] H. Wang, Z. Wu, S. Rahnamayan, Y. Liu, M. Ventresca, Enhancing particle swarm optimization using generalized opposition-based learning, Inf. Sci., 181 (2011), 4699–4714. https://doi.org/10.1016/j.ins.2011.03.016 doi: 10.1016/j.ins.2011.03.016
    [46] M. Abd Elaziz, D. Oliva, S. Xiong, An improved opposition-based sine cosine algorithm for global optimization, Exp. Syst. Appl., 90 (2017), 484–500. https://doi.org/10.1016/j.eswa.2017.07.043 doi: 10.1016/j.eswa.2017.07.043
    [47] W. Long, J. Jiao, X. Liang, S. Cai, M. Xu, A random opposition-based learning grey wolf optimizer, IEEE Access, 7 (2019), 113810–113825. https://doi.org/10.1109/ACCESS.2019.2934994 doi: 10.1109/ACCESS.2019.2934994
    [48] R. Storn, K. Price, Differential evolution–-a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11 (1997), 341–359. https://doi.org/10.1023/A:1008202821328 doi: 10.1023/A:1008202821328
    [49] A. Nickabadi, M. M. Ebadzadeh, R. Safabakhsh, A novel particle swarm optimization algorithm with adaptive inertia weight, Appl. Soft Comput., 11 (2011), 3658–3670. https://doi.org/10.1016/j.asoc.2011.01.037 doi: 10.1016/j.asoc.2011.01.037
    [50] Y. Honshuku, H. Isakari, A topology optimisation of acoustic devices based on the frequency response estimation with the Padé approximation, Appl. Math. Modell., 110 (2022), 819–840. https://doi.org/10.1016/j.apm.2022.06.020 doi: 10.1016/j.apm.2022.06.020
    [51] H. Vazquez-Leal, B. Benhammouda, U. Filobello-Nino, A. Sarmiento-Reyes, V. M. Jimenez-Fernandez, J. L. Garcia-Gervacio, et al., Direct application of Padé approximant for solving nonlinear differential equations, SpringerPlus, 3 (2014), 1–11. https://doi.org/10.1186/2193-1801-3-563 doi: 10.1186/2193-1801-3-563
    [52] I. V. Andrianov, A. Shatrov, Padé approximants, their properties, and applications to hydrodynamic problems, Symmetry, 13 (2021), 1869. https://doi.org/10.3390/sym13101869 doi: 10.3390/sym13101869
    [53] J. Xu, T. Wang, L. Pei, S. Mao, C. Zhu, Parameter identification of electrolyte decomposition state in lithium-ion batteries based on a reduced pseudo two-dimensional model with Padé approximation, J. Power Sources, 460 (2020), 228093. https://doi.org/10.1016/j.jpowsour.2020.228093 doi: 10.1016/j.jpowsour.2020.228093
    [54] A. H. Gandomi, X. S. Yang, Evolutionary boundary constraint handling scheme, Neural Comput. Appl., 21 (2012), 1449–1462. https://doi.org/10.1007/s00521-012-1069-0 doi: 10.1007/s00521-012-1069-0
    [55] A. H. Gandomi, A. R. Kashani, Evolutionary bound constraint handling for particle swarm optimization, in 2016 4th International Symposium on Computational and Business Intelligence (ISCBI), (2016), 148–152. https://doi.org/10.1109/ISCBI.2016.7743274
    [56] A. H. Gandomi, A. R. Kashani, M. Mousavi, Boundary constraint handling affection on slope stability analysis, in Engineering and Applied Sciences Optimization. Computational Methods in Applied Sciences (eds. N. Lagaros and M. Papadrakakis), Springer, (2015), 341–358. https://doi.org/10.1007/978-3-319-18320-6_18
    [57] A. H. Gandomi, A. R. Kashani, F. Zeighami, Retaining wall optimization using interior search algorithm with different bound constraint handling, Int. J. Numer. Anal. Methods Geomech., 41 (2017), 1304–1331. https://doi.org/10.1002/nag.2678 doi: 10.1002/nag.2678
    [58] A. C. Cinar, A comprehensive comparison of accuracy-based fitness functions of metaheuristics for feature selection, Soft Comput., 27 (2023), 8931–8958. https://doi.org/10.1007/s00500-023-08414-3 doi: 10.1007/s00500-023-08414-3
    [59] I. Al-Shourbaji, N. Helian, Y. Sun, S. Alshathri, M. Abd Elaziz, Boosting ant colony optimization with reptile search algorithm for churn prediction, Mathematics, 10 (2022), 1031. https://doi.org/10.3390/math10071031 doi: 10.3390/math10071031
    [60] E. S. M. El-Kenawy, S. Mirjalili, A. Ibrahim, M. Alrahmawy, M. El-Said, R. M. Zaki, et al., Advanced meta-heuristics, convolutional neural networks, and feature selectors for efficient COVID-19 X-ray chest image classification, IEEE Access, 9 (2021). 36019–36037. https://doi.org/10.1109/ACCESS.2021.3061058
    [61] T. Khosla, O. P. Verma, An adaptive hybrid particle swarm optimizer for constrained optimization problem, in 2021 International Conference in Advances in Power, Signal, and Information Technology (APSIT), IEEE, (2021), 1–7. https://doi.org/10.1109/APSIT52773.2021.9641410
    [62] B. D. Kwakye, Y. Li, H. H. Mohamed, E. Baidoo, T. Q. Asenso, Particle guided metaheuristic algorithm for global optimization and feature selection problem, Exp. Syst. Appl., 248 (2024), 123362. https://doi.org/10.1016/j.eswa.2024.123362 doi: 10.1016/j.eswa.2024.123362
    [63] G. Wu, R. Mallipeddi, P. N. Suganthan, Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization, National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report, 2017.
    [64] S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Software, 95 (2016), 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 doi: 10.1016/j.advengsoft.2016.01.008
    [65] 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
    [66] J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN'95-International Conference on Neural Networks, 4 (1995), 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
    [67] S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Software, 69 (2014), 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007 doi: 10.1016/j.advengsoft.2013.12.007
    [68] 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
    [69] L. Abualigah, A. Diabat, S. Mirjalili, M Abd Elaziz, A. H. Gandomi, The arithmetic optimization algorithm, Comput. Methods Appl. Mech. Eng., 376 (2021), 113609. https://doi.org/10.1016/j.cma.2020.113609 doi: 10.1016/j.cma.2020.113609
    [70] S. Arora, P. Anand, Binary butterfly optimization approaches for feature selection, Exp. Syst. Appl., 116 (2019), 147–160. https://doi.org/10.1016/j.eswa.2018.08.051 doi: 10.1016/j.eswa.2018.08.051
    [71] R. Tanabe, A. S. Fukunaga, Improving the search performance of SHADE using linear population size reduction, in 2014 IEEE Congress on Evolutionary Computation (CEC), (2014), 1658–1665. https://doi.org/10.1109/CEC.2014.6900380
    [72] A. W. Mohamed, A. A. Hadi, A. M. Fattouh, K. M. Jambi, LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems, in 2017 IEEE Congress on Evolutionary Computation (CEC), (2017), 145–152. https://doi.org/10.1109/CEC.2017.7969307
    [73] S. Zhao, Y. Wu, S. Tan, J. Wu, Z. Cui, Y. G. Wang, QQLMPA: A quasi-opposition learning and Q-learning based marine predators algorithm, Exp. Syst. Appl., 213 (2023), 119246. https://doi.org/10.1016/j.eswa.2022.119246 doi: 10.1016/j.eswa.2022.119246
    [74] Y. Xu, Z. Yang, X. Li, H. Kang, X. Yang, Dynamic opposite learning enhanced teaching-learning-based optimization, Knowl. Based Syst., 188 (2020), 104966. https://doi.org/10.1016/j.knosys.2019.104966 doi: 10.1016/j.knosys.2019.104966
    [75] R. W. Morrison, Designing Evolutionary Algorithms for Dynamic Environments, Springer, 2004. https://doi.org/10.1007/978-3-662-06560-0
    [76] K. Hussain, M. N. M. Salleh, S. Cheng, Y. Shi, On the exploration and exploitation in popular swarm-based metaheuristic algorithms, Neural Comput. Appl., 31 (2019), 7665–7683. https://doi.org/10.1007/s00521-018-3592-0 doi: 10.1007/s00521-018-3592-0
    [77] S. Cheng, Y. Shi, Q. Qin, Q. Zhang, R. Bai, Population diversity maintenance in brain storm optimization algorithm, J. Artif. Intell. Soft Comput. Res., 4 (2014), 83–97. https://doi.org/10.1515/jaiscr-2015-0001 doi: 10.1515/jaiscr-2015-0001
    [78] S. Mirjalili, A. Lewis, S-shaped versus V-shaped transfer functions for binary particle swarm optimization, Swarm Evol. Comput., 9 (2013), 1–14. https://doi.org/10.1016/j.swevo.2012.09.002 doi: 10.1016/j.swevo.2012.09.002
    [79] J. Too, A. R. Abdullah, N. Mohd-Saad, Hybrid binary particle swarm optimization differential evolution-based feature selection for emg signals classification, Axioms, 8 (2019), 79. https://doi.org/10.3390/axioms8030079 doi: 10.3390/axioms8030079
    [80] J. Too, A. R. Abdullah, N. Mohd-Saad, N. Mohd-Ali, W. Tee, A new competitive binary grey wolf optimizer to solve the feature selection problem in emg signals classification, Computers, 7 (2018), 58. https://doi.org/10.3390/computers7040058 doi: 10.3390/computers7040058
    [81] M. H. Aghdam, N. Ghasem-Aghaee, M. E. Basiri, Text feature selection using ant colony optimization, Exp. Syst. Appl., 36 (2009), 6843–6853. https://doi.org/10.1016/j.eswa.2008.08.022 doi: 10.1016/j.eswa.2008.08.022
    [82] J. Too, A. R. Abdullah, N. Mohd-Saad, A new quadratic binary harris hawk optimization for feature selection, Electronics, 8 (2019), 1130. https://doi.org/10.3390/electronics8101130 doi: 10.3390/electronics8101130
  • Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(409) PDF downloads(54) Cited by(0)

Article outline

Figures and Tables

Figures(19)  /  Tables(27)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog