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To address these issues, this paper proposes a Hybrid Breeding-based Co-evolutionary Ant Colony Optimization method (HBACO) for feature selection. Inspired by the principle of hybrid breeding, in which individuals with distinct traits produce superior offspring through cross recombination, inheritance of desirable genes and continuous evolution, the proposed algorithm establishes a three-population collaborative framework. It consists of an ACO-based search population, an HRO-based evolutionary population and a cooperative feedback population that evolve iteratively together. Furthermore, we devise a heuristic strategy integrating correlation and genetic characteristics to help mine high-value feature subsets. Meanwhile, a collaborative pheromone updating mechanism is adopted to realize efficient knowledge sharing among populations. Experiments conducted on 13 high-dimensional datasets, including Colon and Lung, demonstrate that HBACO achieves superior classification accuracy, feature reduction performance, and convergence behavior compared with 10 representative algorithms. Specifically, HBACO improves the average classification accuracy by 3.9% and achieves an average feature dimensionality reduction rate of 91.4%. Statistical tests further confirm the significance of the proposed method. The results indicate that HBACO provides an effective and robust solution for high-dimensional feature selection problems.<\/jats:p>","DOI":"10.3390\/biomimetics11060404","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:04:14Z","timestamp":1780913054000},"page":"404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High-Dimensional Small-Sample Feature Selection Using Co-Evolutionary Ant Colony Optimization Inspired by Heterosis"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4028-9887","authenticated-orcid":false,"given":"Chunli","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"},{"name":"Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"},{"name":"Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, No. 8 Yangqiaohu Road, Jiangxia District, Wuhan 430205, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9195-1306","authenticated-orcid":false,"given":"Jing","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"},{"name":"Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwei","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"},{"name":"Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zenggang","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Computer and Information Science, Hubei Engineering University, Xiaogan 432000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6431-9866","authenticated-orcid":false,"given":"An","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"},{"name":"Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingfeng","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"},{"name":"Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"},{"name":"Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cai, Y., Yin, F., Hammou, D., and Mantiuk, R. (2025, January 11\u201315). Do computer vision foundation models learn the low-level characteristics of the human visual system?. Proceedings of the Computer Vision and Pattern Recognition Conference, Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.01866"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10479-022-04849-3","article-title":"A novel framework of credit risk feature selection for SMEs during industry 4.0","volume":"350","author":"Lu","year":"2025","journal-title":"Ann. Oper. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fang, T., Wang, J., Ye, M., and Huang, Y. (July, January 30). UMLLA-AD: Mamba-Driven Adaptive Feature Selection for Industrial Anomaly Detection. Proceedings of the 2025 International Conference on Multimedia Retrieval, Chicago, IL, USA.","DOI":"10.1145\/3731715.3733458"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100705","DOI":"10.1016\/j.modpat.2025.100705","article-title":"Future of artificial intelligence\u2014Machine learning trends in pathology and medicine","volume":"38","author":"Hanna","year":"2025","journal-title":"Mod. Pathol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4394","DOI":"10.1109\/TIA.2020.2977872","article-title":"Detection of cyberattacks in industrial control systems using enhanced principal component analysis and hypergraph-based convolution neural network (EPCA-HG-CNN)","volume":"56","author":"Krithivasan","year":"2020","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"10028","DOI":"10.1109\/TNNLS.2025.3547013","article-title":"Efficient Linear Discriminant Analysis Based on Randomized Low-Rank Approaches","volume":"36","author":"Wang","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.knosys.2018.12.015","article-title":"FPCANet: Fisher discrimination for principal component analysis network","volume":"166","author":"Sun","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/JAS.2024.124320","article-title":"Evolutionary optimization methods for high-dimensional expensive problems: A survey","volume":"11","author":"Zhou","year":"2024","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"123300","DOI":"10.1016\/j.ins.2026.123300","article-title":"Optimal activation function subset selection via forward and backward search for enhanced model performance","volume":"742","author":"Son","year":"2026","journal-title":"Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1109\/JBHI.2018.2872811","article-title":"A hybrid feature selection method based on binary state transition algorithm and ReliefF","volume":"23","author":"Huang","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105052","DOI":"10.1016\/j.knosys.2019.105052","article-title":"Distributed multi-label feature selection using individual mutual information measures","volume":"188","author":"Ventura","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106255","DOI":"10.1016\/j.asoc.2020.106255","article-title":"Feature selection based on regularization of sparsity based regression models by hesitant fuzzy correlation","volume":"91","author":"Mokhtia","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107190","DOI":"10.1016\/j.comnet.2020.107190","article-title":"Consistency measure based simultaneous feature selection and instance purification for multimedia traffic classification","volume":"173","author":"Wu","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107459","DOI":"10.1016\/j.measurement.2019.107459","article-title":"Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models","volume":"153","author":"Ergen","year":"2020","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.asoc.2018.11.001","article-title":"GA-SVM based feature selection and parameter optimization in hospitalization expense modeling","volume":"75","author":"Tao","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"122709","DOI":"10.1016\/j.renene.2025.122709","article-title":"Improved MPPT of solar PV Systems under different Environmental conditions utilizes a Novel Hybrid PSO","volume":"244","author":"Yousaf","year":"2025","journal-title":"Renew. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2713","DOI":"10.1007\/s00521-023-09202-8","article-title":"Review of the grey wolf optimization algorithm: Variants and applications","volume":"36","author":"Liu","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3213","DOI":"10.1007\/s11831-025-10240-9","article-title":"Recent advances in Multi-objective Cuckoo Search Algorithm, its variants and applications: SN Makhadmeh et al","volume":"32","author":"Makhadmeh","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102854","DOI":"10.1016\/j.ipm.2021.102854","article-title":"Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems","volume":"59","author":"Ma","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4103","DOI":"10.1007\/s41060-024-00712-9","article-title":"An effective initialization for fuzzy pso with greedy forward selection in feature selection","volume":"20","author":"Mishra","year":"2025","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.19139\/soic-2310-5070-2617","article-title":"Hybrid butterfly-grey Wolf optimization (HB-GWO): A novel metaheuristic approach for feature selection in high-dimensional data","volume":"13","author":"Aly","year":"2025","journal-title":"Stat. Optim. Inf. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"45190","DOI":"10.1038\/s41598-025-29018-x","article-title":"A novel hybrid feature selection method combining binary grey wolf optimization and cuckoo search","volume":"15","author":"Liu","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sezgin, A., Ula\u015f, M., and Boyac\u0131, A. (2025). Multi-Objective Feature Selection for Intrusion Detection Systems: A Comparative Analysis of Bio-Inspired Optimization Algorithms. Sensors, 25.","DOI":"10.3390\/s25196099"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8001","DOI":"10.1038\/s41598-024-78758-9","article-title":"Dynamic niche technology based hybrid breeding optimization algorithm for multimodal feature selection","volume":"15","author":"Cai","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"30741","DOI":"10.1038\/s41598-024-80648-z","article-title":"Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection","volume":"14","author":"Ye","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sancar, N. (2026). A Novel MBPSO\u2013BDGWO Ensemble Feature Selection Method for High-Dimensional Classification Data. Informatics, 13.","DOI":"10.3390\/informatics13010007"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Z., Fu, B., and Yang, Y. (2025). A Novel Elite-Guided Hybrid Metaheuristic Algorithm for Efficient Feature Selection. Biomimetics, 10.","DOI":"10.3390\/biomimetics10110747"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1007\/s11831-024-10178-4","article-title":"Multi-objective Ant Colony Optimization: Review: MA Awadallah et al","volume":"32","author":"Awadallah","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105285","DOI":"10.1016\/j.knosys.2019.105285","article-title":"MLACO: A multi-label feature selection algorithm based on ant colony optimization","volume":"192","author":"Paniri","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109874","DOI":"10.1016\/j.knosys.2022.109874","article-title":"Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification","volume":"256","author":"Wang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5276","DOI":"10.1109\/TCYB.2022.3185554","article-title":"Information-theory-based nondominated sorting ant colony optimization for multiobjective feature selection in classification","volume":"53","author":"Wang","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_32","first-page":"122","article-title":"Multi-label feature selection using density-based graph clustering and ant colony optimization","volume":"10","author":"Kakarash","year":"2023","journal-title":"J. Comput. Des. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"119130","DOI":"10.1016\/j.eswa.2022.119130","article-title":"SemiACO: A semi-supervised feature selection based on ant colony optimization","volume":"214","author":"Karimi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100892","DOI":"10.1016\/j.swevo.2021.100892","article-title":"Ant-TD: Ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection","volume":"64","author":"Paniri","year":"2021","journal-title":"Swarm Evol. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21777","DOI":"10.1038\/s41598-024-71932-z","article-title":"Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization","volume":"14","author":"Xia","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ma, X., and Liu, C. (2024). An Attention Ant Colony Algorithm for Solving SDVRP. Proceedings of the 2024 36th Chinese Control and Decision Conference (CCDC), Xi\u2019an, China, 25\u201327 May 2024, IEEE.","DOI":"10.1109\/CCDC62350.2024.10588134"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5888","DOI":"10.1038\/s41598-025-89961-7","article-title":"HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification","volume":"15","author":"Agarwal","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s43067-025-00243-8","article-title":"Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm","volume":"12","author":"Sarhan","year":"2025","journal-title":"J. Electr. Syst. Inf. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s11227-026-08409-5","article-title":"SAMACO_FS: Feature selection for high-dimensional few instances using ant colony optimization algorithm and self-attention mechanism","volume":"82","author":"Ye","year":"2026","journal-title":"J. Supercomput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"114419","DOI":"10.1016\/j.knosys.2025.114419","article-title":"Hboffs: Hybrid breeding optimization algorithm inspired federated feature selection for intrusion detection in iiot","volume":"329","author":"Ye","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ye, Z., Zhang, S., Zhou, W., Wu, L., Shen, J., Cai, T., Wang, M., and Zhang, J. (2026, January 20\u201327). Incomplete Multi-View Unsupervised Federated Feature Selection via Cooperative Particle Swarm Optimization and Tensor-Aligned Learning. Proceedings of the AAAI Conference on Artificial Intelligence, Singapore.","DOI":"10.1609\/aaai.v40i33.40005"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"111901","DOI":"10.1016\/j.patcog.2025.111901","article-title":"A cooperative hybrid breeding swarm intelligence algorithm for feature selection","volume":"169","author":"Mei","year":"2026","journal-title":"Pattern Recognit."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/6\/404\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:09:13Z","timestamp":1780916953000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/6\/404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,8]]},"references-count":42,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["biomimetics11060404"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics11060404","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,8]]}}}