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Traditional neural networks often suffer from difficulties in parameter optimization and local optima, while the performance of approximate logical dendritic neuron models depends heavily on proper parameter configuration. To address these issues, this paper proposes a Feature-Guided Adaptive Differential Evolution algorithm, which incorporates feature-guided mechanisms, adaptive parameter control, and multi-strategy mutation to enhance the standard differential evolution framework. The algorithm is applied to optimize the dendritic neuron model\u2019s parameters, providing a more effective and robust solution for intelligent classification in complex data environments. This approach contributes to the development of reliable optimization strategies in intelligent systems and has promising application potential.<\/jats:p>","DOI":"10.1515\/pjbr-2025-0013","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T15:06:12Z","timestamp":1761145572000},"source":"Crossref","is-referenced-by-count":0,"title":["Approximate logic dendritic neuron model classification based on improved DE algorithm"],"prefix":"10.1515","volume":"16","author":[{"given":"Chunxia","family":"Liu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Zhengzhou Railway Vocational and Technical College , Zhengzhou , 450000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"2025102215060758059_j_pjbr-2025-0013_ref_001","doi-asserted-by":"crossref","unstructured":"S. Li, D. W. McLaughlin, and D. Zhou, \u201cMathematical modeling and analysis of spatial neuron dynamics: Dendritic integration and beyond,\u201d Commun. Pure Appl. Math., vol. 76, no. 1, pp. 114\u2013162, 2023.","DOI":"10.1002\/cpa.22020"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_002","doi-asserted-by":"crossref","unstructured":"X. Luo, X. Wen, Y. Li, and Q. Li, \u201cPruning method for dendritic neuron model based on dendrite layer significance constraints,\u201d CAAI Trans. Intell. Technol., vol. 8, no. 2, pp. 308\u2013318, 2023.","DOI":"10.1049\/cit2.12234"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_003","doi-asserted-by":"crossref","unstructured":"A. Yilmaz and U. Yolcu, \u201cA robust training of dendritic neuron model neural network for time series prediction,\u201d Neural Comput. Appl., vol. 35, no. 14, pp. 10387\u201310406, 2023.","DOI":"10.1007\/s00521-023-08240-6"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_004","doi-asserted-by":"crossref","unstructured":"S. Ji and J. Karlov\u0161ek, \u201cOptimized differential evolution algorithm for solving DEM material calibration problem,\u201d Eng. Comput., vol. 39, no. 3, pp. 2001\u20132016, 2023.","DOI":"10.1007\/s00366-021-01564-8"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_005","doi-asserted-by":"crossref","unstructured":"M. K. Kar, S. Kumar, A. K. Singh, and S. Panigrahi, \u201cReactive power management by using a modified differential evolution algorithm,\u201d Opt. Control. Appl. Methods, vol. 44, no. 2, pp. 967\u2013986, 2023.","DOI":"10.1002\/oca.2815"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_006","doi-asserted-by":"crossref","unstructured":"C. Hebbi and H. Mamatha, \u201cComprehensive dataset building and recognition of isolated handwritten Kannada characters using machine learning models,\u201d Artif. Intell. Appl., vol. 1, no. 3, pp. 179\u2013190, 2023.","DOI":"10.47852\/bonviewAIA3202624"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_007","doi-asserted-by":"crossref","unstructured":"J. Ji, J. Zhao, Q. Lin, and K. C. Tan, \u201cCompetitive decomposition-based multiobjective architecture search for the dendritic neural model,\u201d IEEE Trans. Cybern., vol. 53, no. 11, pp. 6829\u20136842, 2022.","DOI":"10.1109\/TCYB.2022.3165374"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_008","doi-asserted-by":"crossref","unstructured":"M. I. Georgescu, R. T. Ionescu, N. C. Ristea, and N. Sebe, \u201cNonlinear neurons with human-like apical dendrite activations,\u201d Appl. Intell., vol. 53, no. 21, pp. 25984\u201326007, 2023.","DOI":"10.1007\/s10489-023-04921-w"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_009","doi-asserted-by":"crossref","unstructured":"N. Zhang and Y. Yan, \u201cApproximate logical dendritic neuron model based on selection operator improved differential evolution algorithm,\u201d Acad. J. Comput. Inf. Sci., vol. 6, no. 5, pp. 113\u2013122, 2023.","DOI":"10.25236\/AJCIS.2023.060516"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_010","doi-asserted-by":"crossref","unstructured":"E. K\u00f6lemen, \u201cForecasting of Turkey\u2019s Hazelnut export amounts according to seasons with dendritic neuron model artificial neural network,\u201d Turk. J. Forecast., vol. 8, no. 2, pp. 1\u20137, 2024.","DOI":"10.34110\/forecasting.1468420"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_011","doi-asserted-by":"crossref","unstructured":"H. Zhang, T. Liu, X. Ye, A. A. Heidari, G. Liang, H. Chen, et al., \u201cDifferential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems,\u201d Eng. Comp., vol. 39, no. 3, pp. 1735\u20131769, 2023.","DOI":"10.1007\/s00366-021-01545-x"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_012","doi-asserted-by":"crossref","unstructured":"S. Chakraborty, A. K. Saha, A. E. Ezugwu, J. O. Agushaka, R. A. Zitar, and L. Abualigah, \u201cDifferential evolution and its applications in image processing problems: A comprehensive review,\u201d Arch. Comput. Methods Eng., vol. 30, no. 2, pp. 985\u20131040, 2023.","DOI":"10.1007\/s11831-022-09825-5"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_013","doi-asserted-by":"crossref","unstructured":"A. M. Ibrahim and M. A. Tawhid, \u201cAn improved artificial algae algorithm integrated with differential evolution for job-shop scheduling problem,\u201d J. Intell. Manuf., vol. 34, no. 4, pp. 1763\u20131778, 2023.","DOI":"10.1007\/s10845-021-01888-8"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_014","doi-asserted-by":"crossref","unstructured":"A. Thakare, A. M. Anter, and A. 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Res., vol. 62, no. 12, pp. 4226\u20134244, 2024.","DOI":"10.1080\/00207543.2023.2254858"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_017","doi-asserted-by":"crossref","unstructured":"X. Lu and S. Xu, \u201cPerformance optimization of vertical axis wind turbine based on Taguchi method, improved differential evolution algorithm and Kriging model,\u201d Energy Sources Part. A: Recov. Util. Environ. Eff., vol. 46, no. 1, pp. 2792\u20132810, 2024.","DOI":"10.1080\/15567036.2024.2308655"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_018","doi-asserted-by":"crossref","unstructured":"A. Dey, S. Bhattacharyya, S. Dey, J. Platos, and V. Snasel, \u201cA quantum inspired differential evolution algorithm for automatic clustering of real life datasets,\u201d Multimed. Tools Appl., vol. 83, no. 3, pp. 8469\u20138498, 2024.","DOI":"10.1007\/s11042-023-15704-3"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_019","doi-asserted-by":"crossref","unstructured":"T. Li, Y. Meng, and L. Tang, \u201cScheduling of continuous annealing with a multi-objective differential evolution algorithm based on deep reinforcement learning,\u201d IEEE Trans. Autom. Sci. Eng., vol. 21, no. 2, pp. 1767\u20131780, 2023.","DOI":"10.1109\/TASE.2023.3244331"},{"key":"2025102215060758059_j_pjbr-2025-0013_ref_020","doi-asserted-by":"crossref","unstructured":"J. Niu, Z. Liu, Q. Pan, Y. Yang, and Y. Li, \u201cConditional self-attention generative adversarial network with differential evolution algorithm for imbalanced data classification,\u201d Chin. J. 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