Abstract
A critical challenge in swarm intelligence is the effective utilization of knowledge gained during the search, a process often confounded by the risk of negative knowledge transfer. To address this, we introduce the Learning-Aided Artificial Bee Colony (LA-ABC), a novel framework guided by a Neural Knowledge Transfer mechanism for global optimization. Our framework establishes a co-evolutionary mechanism between the search process of the ABC algorithm and an online neural knowledge learning engine. LA-ABC operates on a dual-pathway architecture, probabilistically arbitrating between foundational swarm exploration and a knowledge-transfer pathway. In this second pathway, an Artificial Neural Network (ANN) learns a predictive, non-linear model from a dynamic archive of historically successful solutions. This approach enables the model to interpret the complex context of successful moves, thereby preventing the negative knowledge transfer where a beneficial pattern in one region of the search space could be detrimental in another. This learned intelligence is then operationalized through a generative operator that transfers validated positive knowledge to create high-quality candidate solutions. The process transforms the ABC from a memoryless explorer into an intelligent agent that learns to navigate the fitness landscape with high efficacy. The superiority of the LA-ABC framework is demonstrated through comprehensive benchmarking on 23 standard test functions, the competitive IEEE CEC 2019 suite, and a real-world photovoltaic parameter extraction problem. Our proposed neural knowledge transfer approach significantly outperforms 12 state-of-the-art algorithms, including ABC, L-SHADE, JSO, L-DE, L-PSO, KL-variants, and RL variants with the significance of these improvements validated by rigorous statistical tests (Wilcoxon, Bonferroni-Dunn, Friedman, and ANOVA). Ultimately, LA-ABC provides a robust new paradigm for integrating reinforcement learning and knowledge transfer within evolutionary computation.
Data availability
All data genrated or analysed during this study are included in this published article.
References
Back, T. Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms, (Oxford university press, 1996).
Li, J.-Y., Zhan, Z.-H. & Zhang, J. Evolutionary computation for expensive optimization: A survey. Machine Intelligence Research 19(1), 3–23 (2022).
Zhan, Z.-H., Shi, L., Tan, K. C. & Zhang, J. A survey on evolutionary computation for complex continuous optimization. Artif. Intell. Rev. 55(1), 59–110 (2022).
Holland, J. H. Genetic algorithms. Sci. Am. 267(1), 66–73 (1992).
Storn, R. & Price, K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997).
Eberhart, R. & Kennedy, J. Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks 2, 1942–1948 (1995).
Karaboga, D. & Basturk, B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J. Global Optim. 39(3), 459–471 (2007).
Chawla, M. & Duhan, M. Applications of recent metaheuristics optimisation algorithms in biomedical engineering: a review. Int. J. Biomed. Eng. Technol. 16(3), 268–278 (2014).
Arias-Montano, A., Coello, C. A. C. & Mezura-Montes, E. Multiobjective evolutionary algorithms in aeronautical and aerospace engineering. IEEE Trans. Evol. Comput. 16(5), 662–694 (2012).
Helmi, A. M., Carli, R., Dotoli, M. & Ramadan, H. S. Efficient and sustainable reconfiguration of distribution networks via metaheuristic optimization. IEEE Trans. Autom. Sci. Eng. 19(1), 82–98 (2021).
Rajwar, K., Deep, K. & Das, S. An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artif. Intell. Rev. 56(11), 13187–13257 (2023).
D. Karaboga, et al., An idea based on honey bee swarm for numerical optimization (2005).
Jin, Y. Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm Evol. Comput. 1(2), 61–70 (2011).
Ong, Y. S., Nair, P. B. & Keane, A. J. Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA J. 41(4), 687–696 (2003).
Zhan, Z.-H., Li, J.-Y., Kwong, S. & Zhang, J. Learning-aided evolution for optimization. IEEE Trans. Evol. Comput. 27(6), 1794–1808 (2022).
Zhang, J. & Sanderson, A. C. Jade: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009).
Zhan, Z.-H., Wang, Z.-J., Jin, H. & Zhang, J. Adaptive distributed differential evolution. IEEE transactions on cybernetics 50(11), 4633–4647 (2019).
J. Brest, M. S. Maučec, B. Bošković, Single objective real-parameter optimization: Algorithm jso, in: 2017 IEEE congress on evolutionary computation (CEC), 1311–1318 (IEEE, 2017).
Lezama, F., Soares, J., Faia, R., & Vale, Z. Hybrid-adaptive differential evolution with decay function (hyde-df) applied to the 100-digit challenge competition on single objective numerical optimization, in: Proceedings of the genetic and evolutionary computation conference companion, 7–8 (2019).
Liang, J. J., Guo, L., Liu, R., & Qu, B. Y., A self-adaptive dynamic particle swarm optimizer, in: 2015 IEEE Congress on Evolutionary Computation (CEC), 3206–3213 (IEEE, 2015)
Ratnaweera, A., Halgamuge, S. K. & Watson, H. C. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004).
Xia, X. et al. Triple archives particle swarm optimization. IEEE transactions on cybernetics 50(12), 4862–4875 (2019).
Liu, W. et al. A novel sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE transactions on cybernetics 51(2), 1085–1093 (2019).
Jiang, Y., Zhan, Z. H., Tan, K. C., & Zhang, J. Knowledge learning for evolutionary computation, IEEE transactions on evolutionary computation (2023).
Tan, K. C., Feng, L. & Jiang, M. Evolutionary transfer optimization-a new frontier in evolutionary computation research. IEEE Comput. Intell. Mag. 16(1), 22–33 (2021).
Xue, X. et al. Solution transfer in evolutionary optimization: An empirical study on sequential transfer. IEEE Trans. Evol. Comput. 28(6), 1776–1793 (2023).
Guo, Y., Chen, G., Jiang, M., Gong, D. & Liang, J. A knowledge guided transfer strategy for evolutionary dynamic multiobjective optimization. IEEE Trans. Evol. Comput. 27(6), 1750–1764 (2022).
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y., Deep learning, Vol. 1, (MIT press Cambridge, 2016).
Stanley, K. O., Clune, J., Lehman, J. & Miikkulainen, R. Designing neural networks through neuroevolution. Nature Machine Intelligence 1(1), 24–35 (2019).
Burke, E. K. et al. Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society 64(12), 1695–1724 (2013).
Li, J.-Y., Zhan, Z.-H., Xu, J., Kwong, S. & Zhang, J. Surrogate-assisted hybrid-model estimation of distribution algorithm for mixed-variable hyperparameters optimization in convolutional neural networks. IEEE Transactions on Neural Networks and Learning Systems 34(5), 2338–2352 (2021).
Xu, D. & Cao, B. Adaptive multiobjective evolutionary generative adversarial network for metaverse network intrusion detection. Research 8, 0665 (2025).
Galván, E. & Mooney, P. Neuroevolution in deep neural networks: Current trends and future challenges. IEEE Transactions on Artificial Intelligence 2(6), 476–493 (2021).
Zhan, Z.-H., Li, J.-Y. & Zhang, J. Evolutionary deep learning: A survey. Neurocomputing 483, 42–58 (2022).
Larrañaga, P., & Bielza, C. Estimation of distribution algorithms in machine learning: a survey, IEEE transactions on evolutionary computation (2023).
Mishra, V. & Kane, L. A survey of designing convolutional neural network using evolutionary algorithms. Artif. Intell. Rev. 56(6), 5095–5132 (2023).
Zhang, J. et al. Evolutionary computation meets machine learning: A survey. IEEE Comput. Intell. Mag. 6(4), 68–75 (2011).
Karaboga, D., Gorkemli, B., Ozturk, C. & Karaboga, N. A comprehensive survey: artificial bee colony (abc) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014).
Ozturk, C., Karaboga, D. & Hybrid artificial bee colony algorithm for neural network training, in,. IEEE congress of evolutionary computation (CEC). IEEE 2011, 84–88 (2011).
Gao, W.-F. & Liu, S.-Y. A modified artificial bee colony algorithm. Computers & Operations Research 39(3), 687–697 (2012).
Wang, H. et al. Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 279, 587–603 (2014).
Kiran, M. S., Hakli, H., Gunduz, M. & Uguz, H. Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf. Sci. 300, 140–157 (2015).
Gao, W.-F. et al. Artificial bee colony algorithm with multiple search strategies. Appl. Math. Comput. 271, 269–287 (2015).
Zhou, X., Wu, Z., Wang, H. & Rahnamayan, S. Gaussian bare-bones artificial bee colony algorithm. Soft. Comput. 20, 907–924 (2016).
Jadon, S. S., Tiwari, R., Sharma, H. & Bansal, J. C. Hybrid artificial bee colony algorithm with differential evolution. Appl. Soft Comput. 58, 11–24 (2017).
Jadon, S. S., Sharma, H., Kumar, E., & Bansal, J. C., Application of binary particle swarm optimization in cryptanalysis of des, in: Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011 1, 1061–1071 (Springer, 2012).
Garg, N., Jadon, S. S., Sharma, H., & Palwalia, Gbest-artificial bee colony algorithm to solve load flow problem, in: Proceedings of the Third International Conference on Soft Computing for Problem Solving: SocProS 2013, 2, 529–538 (Springer, 2014).
Zhou, X., Wang, H., Wang, M. & Wan, J. Enhancing the modified artificial bee colony algorithm with neighborhood search. Soft. Comput. 21(10), 2733–2743 (2017).
Liang, Z., Hu, K., Zhu, Q. & Zhu, Z. An enhanced artificial bee colony algorithm with adaptive differential operators. Appl. Soft Comput. 58, 480–494 (2017).
Kumar, D. & Mishra, K. Portfolio optimization using novel co-variance guided artificial bee colony algorithm. Swarm Evol. Comput. 33, 119–130 (2017).
Jadon, S. S., Bansal, J. C., Tiwari, R. & Sharma, H. Artificial bee colony algorithm with global and local neighborhoods. International Journal of System Assurance Engineering and Management 9, 589–601 (2018).
Jadon, S. S., Sharma, H., Tiwari, R. & Bansal, J. C. Self-adaptive position update in artificial bee colony. International Journal of System Assurance Engineering and Management 9, 802–810 (2018).
Xiang, W.-L., Meng, X.-L., Li, Y.-Z., He, R.-C. & An, M.-Q. An improved artificial bee colony algorithm based on the gravity model. Inf. Sci. 429, 49–71 (2018).
Kumar, D. & Mishra, K. Co-variance guided artificial bee colony. Appl. Soft Comput. 70, 86–107 (2018).
Han, Z., Chen, M., Shao, S. & Wu, Q. Improved artificial bee colony algorithm-based path planning of unmanned autonomous helicopter using multi-strategy evolutionary learning. Aerosp. Sci. Technol. 122, 107374 (2022).
Özdemir, D., Dörterler, S. & Aydın, D. A new modified artificial bee colony algorithm for energy demand forecasting problem. Neural Comput. Appl. 34(20), 17455–17471 (2022).
Zhang, Y., Pang, B., Song, Y., Xu, Q. & Yuan, X. Artificial bee colony algorithm based on dimensional memory mechanism and adaptive elite population for training artificial neural networks. IEEE Access 11, 107616–107637 (2023).
Lamjiak, T., Sirinaovakul, B., Kornthongnimit, S., Polvichai, J. & Sohail, A. Optimizing artificial neural network learning using improved reinforcement learning in artificial bee colony algorithm. Applied Computational Intelligence and Soft Computing 2024(1), 6357270 (2024).
Zhu, F. et al. obabc: a one-dimensional binary artificial bee colony algorithm for binary optimization. Swarm Evol. Comput. 87, 101567 (2024).
Yang, J., Li, W.-T., Shi, X.-W., Xin, L. & Yu, J.-F. A hybrid abc-de algorithm and its application for time-modulated arrays pattern synthesis. IEEE Trans. Antennas Propag. 61(11), 5485–5495 (2013).
Saini, G., & Jadon, S. S. , A comprehensive review of hybrid variants of artificial bee colony algorithm, High-Performance Automation Methods for Computational Intelligent Systems: Challenges, Opportunities, and Applications, 148 (2025).
Gupta, A., Ong, Y.-S. & Feng, L. Multifactorial evolution: Toward evolutionary multitasking. IEEE Trans. Evol. Comput. 20(3), 343–357 (2015).
Zhang, F., Mei, Y., Nguyen, S., Zhang, M. & Tan, K. C. Surrogate-assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling. IEEE Trans. Evol. Comput. 25(4), 651–665 (2021).
Li, J.-Y., Zhan, Z.-H., Tan, K. C. & Zhang, J. A meta-knowledge transfer-based differential evolution for multitask optimization. IEEE Trans. Evol. Comput. 26(4), 719–734 (2021).
Yokoya, G., Xiao, H., Hatanaka, T. & Multifactorial optimization using artificial bee colony and its application to car structure design optimization, in,. IEEE Congress on Evolutionary Computation (CEC). IEEE 2019, 3404–3409 (2019).
Bian, X. et al. Multitask particle swarm optimization algorithm leveraging variable chunking and local meta-knowledge transfer. Swarm Evol. Comput. 92, 101823 (2025).
Mallipeddi, R., Suganthan, P. N., Pan, Q.-K. & Tasgetiren, M. F. Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679–1696 (2011).
Price, K., Awad, N., Ali, M., & Suganthan, P. Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization, in: Technical report, (Nanyang Technological University Singapore, 2018).
Saini, G. & Jadon, S. S. An improved artificial bee colony algorithm based on spider monkey optimization global search for complex benchmarks and engineering applications. Phys. Scr. 100(7), 075229 (2025).
Tanabe, R., Fukunaga, A. S. & Improving the search performance of shade using linear population size reduction, in,. IEEE congress on evolutionary computation (CEC). IEEE 2014, 1658–1665 (2014).
Song, Y. et al. Rl-ga: A reinforcement learning-based genetic algorithm for electromagnetic detection satellite scheduling problem. Swarm Evol. Comput. 77, 101236 (2023).
Zhao, F. et al. A multi-agent reinforcement learning driven artificial bee colony algorithm with the central controller. Expert Syst. Appl. 219, 119672 (2023).
Cui, Y., Hu, W. & Rahmani, A. A reinforcement learning based artificial bee colony algorithm with application in robot path planning. Expert Syst. Appl. 203, 117389 (2022).
Li, Y., Gong, W. & Li, S. Multitasking optimization via an adaptive solver multitasking evolutionary framework. Inf. Sci. 630, 688–712 (2023).
Zhang, T., Gong, W. & Li, Y. Multitask differential evolution with adaptive dual knowledge transfer. Appl. Soft Comput. 165, 112040 (2024).
Acknowledgements
The authors are thankful to the Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India, for the necessary support for this research.
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Open access funding provided by Symbiosis International (Deemed University).
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1.) Gurmeet Saini conceived the core idea of the LA-ABC framework, developed the methodology, carried out the implementation, wrote the main manuscript, and performed the experimental analysis. 2.) Shimpi Singh Jadon supervised the research, provided critical guidance on algorithmic design, and contributed to the interpretation and refinement of results. 3.) Shshank Chaube assisted with experimental validation, statistical analysis, and contributed to manuscript revision. 4.) All authors discussed the results, reviewed the manuscript critically, and approved the final version for submission.
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Saini, G., Jadon, S.S. & Chaube, S. Learning-aided Artificial Bee Colony with neural knowledge transfer for global optimization. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38028-2
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DOI: https://doi.org/10.1038/s41598-026-38028-2
