References
Abdel-Basset, M., Mohamed, R., Jameel, M., & Abouhawwash, M. (2023). Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems. Knowledge-Based Systems, 262, 110248. Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570. Ahmad, M. F., Isa, N. A. M., Lim, W. H., & Ang, K. M. (2022). Differential evolution: A recent review based on state-of-the-art works. Alexandria Engineering Journal, 61(5), 3831– 3872. Akilo, B. E., Oyedotun, S. A., Oise, G. P., Nwabuokei, O. C., & Unuigbokhai, N. B. (2024). Intelligent traffic management system using ant colony and deep learning algorithms for real-time traffic flow optimization. Journal of Science Research and Reviews, 1(2), 63–71. Altalhan, M., Algarni, A., & Alouane, M. T.-H. (2025). Imbalanced data problem in machine learning: A review. IEEE Access, 13, 13686–13699. Chen, S., Ma, L., & Ma, Y. (2020). Distributed set-membership filtering for nonlinear systems subject to round-robin protocol and stochastic communication protocol over sensor networks. Neurocomputing, 385, 13–21. Chen, Z., Liu, H., & Liu, G. (2024). Real-Time Policy Optimization for UAV Swarms Based on Evolution Strategies. Drones, 8(11), 619. Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., & Billinge, S. J. (2022). Recent advances and applications of deep learning methods in materials science. Npj Computational Materials, 8(1), 59. Dashora, M., Sharma, P., & Bhargava, A. (2020). Credit card fraud detection using PSO optimized neural network. International Journal of Engineering and Advanced Technology , 9(4), 360–363. Dey, N., Ashour, A. S., & Bhattacharyya, S. (Eds.). (2020). Applied Nature-Inspired Computing: Algorithms and Case Studies. Springer Singapore. https://doi.org/10.1007/978-981-13- 9263-4 Dritsas, E., & Trigka, M. (2025). Exploring the intersection of machine learning and big data: A survey. Machine Learning and Knowledge Extraction, 7(1), 13. El Habib Kahla, M., Beggas, M., Laouid, A., & Hammoudeh, M. (2024). A nature-inspired partial distance-based clustering algorithm. Journal of Sensor and Actuator Networks, 13(4), 36. Fakhouri, H. N., Ishtaiwi, A., Makhadmeh, S. N., Al-Betar, M. A., & Alkhalaileh, M. (2024). Novel hybrid crayfish optimization algorithm and self-adaptive differential evolution for solving complex optimization problems. Symmetry, 16(7), 927. Guilmeau, T., Chouzenoux, E., & Elvira, V. (2021). Simulated annealing: A review and a new scheme. 2021 IEEE Statistical Signal Processing Workshop , 101–105. https://ieeexplore.ieee.org/abstract/document/9513782/ Guo, Z., Xia, Y., Li, J., Liu, J., & Xu, K. (2024). Hybrid optimization path planning method for AGV based on KGWO. Sensors, 24(18), 5898. Han, Y., Zeng, F., Fu, L., & Zheng, F. (2025). GA-PSO algorithm for microseismic source location. Applied Sciences, 15(4), 1841. Ibrahim, A. O., Elfadel, E. M. E., Hashem, I. A. T., Syed, H. J., Ismail, M. A., Osman, A. H., & Ahmed, A. (2025). The Artificial Bee Colony Algorithm: A Comprehensive Survey of Variants, Modifications, Applications, Developments, and Opportunities. Archives of IJCSMT Computational Methods in Engineering, 32(6), 3499–3533. https://doi.org/10.1007/s11831-025-10269-w Ikotun, A. M., Almutari, M. S., & Ezugwu, A. E. (2021). K-means-based nature-inspired metaheuristic algorithms for automatic data clustering problems: Recent advances and future directions. Applied Sciences, 11(23), 11246. Ikotun, A. M., & Ezugwu, A. E. (2022). Boosting k-means clustering with symbiotic organisms search for automatic clustering problems. PLoS One, 17(8), e0272861. Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210. Jakšić, Z., Devi, S., Jakšić, O., & Guha, K. (2023). A comprehensive review of bio-inspired optimization algorithms including applications in microelectronics and nanophotonics. Biomimetics, 8(3), 278. Jumaah, M. A., Ali, Y. H., & Rashid, T. A. (2025). An improved FOX optimization algorithm using adaptive exploration and exploitation for global optimization. Plos One, 20(9), e0331965. Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80(5), 8091–8126. Martarelli, N. J., & Nagano, M. S. (2020). Unsupervised feature selection based on bio-inspired approaches. Swarm and Evolutionary Computation, 52, 100618. Mirjalili, S., Song Dong, J., & Lewis, A. (Eds.). (2020). Nature-Inspired Optimizers: Theories, Literature Reviews and Applications (Vol. 811). Springer International Publishing. https://doi.org/10.1007/978-3-030-12127-3 Modey, P., Abdul-Salaam, G., Freeman, E., Acheampong, P., Brown-Acquaye, W. L., Agbehadji, I. E., & Millham, R. C. (2024). K-Means Based Bee Colony Optimization for Clustering in Heterogeneous Sensor Network. Sensors, 24(23), 7603. Mollajafari, M., & Ebrahimi-Nejad, S. (2025). Automotive Engineering by Using Evolutionary Algorithms and Nature-Inspired Heuristic. Vehicle Technology and Automotive Engineering, 1, 191. Oyelade, O. N., Ezugwu, A. E.-S., Mohamed, T. I., & Abualigah, L. (2022). Ebola optimization search algorithm: A new nature-inspired metaheuristic optimization algorithm. Ieee Access, 10, 16150–16177. Rajendran, S., Rajagopal, S. K., Thanarajan, T., Shankar, K., Kumar, S., Alsubaie, N. M., Ishak, M. K., & Mostafa, S. M. (2023). Automated segmentation of brain tumor MRI images using deep learning. IEEE Access, 11, 64758–64768. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248. Shaikh, M. S., Lin, H., Zheng, G., Wang, C., & Dong, X. (2024). Innovative hybrid grey wolf- particle swarm optimization for calculating transmission line parameter. Heliyon, 10(19). https://www.cell.com/heliyon/fulltext/S2405-8440(24)14586-0 Sharma, R., Matharu, J. S., & Parmar, K. S. (2025). A survey on Particle Swarm Optimization: Evolution, adaptations and practical implementations. Applied Soft Computing, 114016. Shehab, M., Sihwail, R., Daoud, M. S., Almimi, H. M., & Abualigah, L. (2024). Nature-inspired metaheuristic algorithms: A comprehensive review. Int. Arab J. Inf. Technol., 21(5), 815– 831. Sikka, J., Satya, K., Kumar, Y., Uppal, S., Shah, R. R., & Zimmermann, R. (2020). Learning Based Methods for Code Runtime Complexity Prediction. In J. M. Jose, E. Yilmaz, J. Magalhães, IJCSMT P. Castells, N. Ferro, M. J. Silva, & F. Martins (Eds.), Advances in Information Retrieval (Vol. 12035, pp. 313–325). Springer International Publishing. https://doi.org/10.1007/978- 3-030-45439-5_21 Somvanshi, S., Islam, M. M., Javed, S. A., Chhetri, G., Islam, K. S., Chowdhury, T. I., Polock, S. B. B., Dutta, A., & Das, S. (2025). A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs (arXiv:2506.04238). arXiv. https://doi.org/10.48550/arXiv.2506.04238 Vakhnin, A., Ryzhikov, I., Niska, H., & Kolehmainen, M. (2024). A Novel Multi-Objective Hybrid Evolutionary-Based Approach for Tuning Machine Learning Models in Short-Term Power Consumption Forecasting. AI, 5(4), 2461–2496. Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2021). Understanding deep learning requires rethinking generalization. Communications of the ACM, 64(3), 107–115. https://doi.org/10.1145/3446776 Zhang, T., & Geem, Z. W. (2019). Review of harmony search with respect to algorithm structure. Swarm and Evolutionary Computation, 48, 31–43. Zhu, H., Wang, W., Ulidowski, I., Zhou, Q., Wang, S., Chen, H., & Zhang, Y. (2023). MEEDNets: Medical image classification via ensemble bio-inspired evolutionary DenseNets. Knowledge-Based Systems, 280, 111035.