Hybrid Algorithmic Approaches for Cross-Disciplinary Computational Modelling and Decision Making
DOI:
https://doi.org/10.63503/j.ijcma.2025.172Keywords:
Hybrid Algorithms, Computational Modeling, Decision Making, Evolutionary Optimization, Predictive ModelingAbstract
Hybrid algorithmic methods have become popular to solve complex, cross-disciplinary computational modeling and decision-making problems. This paper suggests a new hybrid algorithm, which combines evolutionary optimization and machine learning-based predictive modelling to enhance the accuracy of solutions, the rate of convergence and the robustness of decisions. The framework was tested on benchmark datasets of engineering design, financial risk assessment and in healthcare decision-making scenarios. The experimental outcomes indicate that the hybrid method is superior to the traditional versions of evolutionary algorithms and individual predictive models, showing an average of 12.5 % improvement in the accuracy of solutions, 18% lower convergence and 9% less computational cost. Also, the sensitivity analysis shows the flexibility of the framework to the levels of complexity of problems, which guarantees the stability of performance in different spheres. Integration of predictive modeling increases the interpretability of the decision and therefore the framework can be used in the real-life scenario where high-stakes decisions are required. On the whole, this work will offer scalable, efficient and interpretable hybrid algorithmic approach which can be used to form the basis of cross-disciplinary computational problem solving.
References
[1] Maluleke, T., Benecke, R., Oladejo, S., Feulner, G., Kern, S., Lister, J., ... & Pillai, G. (2024). Cross‐disciplinary mathematical modelling to benefit healthcare–could clinical pharmacology play an enabling role?. British Journal of Clinical Pharmacology, 90(10), 2509-2516. https://doi.org/10.1111/bcp.16192
[2] Kulwant, M., Raj, D., & Yadav, A. K. Future Perspectives and Challenges in Computational Environmental Engineering. Computational Techniques in Environmental Engineering, 273-282. https://doi.org/10.1201/9781003605553
[3] Razavi, S., Duffy, A., Eamen, L., Jakeman, A. J., Jardine, T. D., Wheater, H., ... & Grimm, V. (2025). Convergent and transdisciplinary integration: On the future of integrated modeling of human‐water systems. Water Resources Research, 61(2), e2024WR038088.
https://doi.org/10.1029/2024WR038088
[4] Bohrer, J. D. S., & Dorn, M. (2024). Enhancing classification with hybrid feature selection: A multi-objective genetic algorithm for high-dimensional data. Expert Systems with Applications, 255, 124518. https://doi.org/10.1016/j.eswa.2024.124518
[5] Grosan, C., & Abraham, A. (2007). Hybrid evolutionary algorithms: methodologies, architectures, and reviews. In Hybrid evolutionary algorithms (pp. 1-17). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-73297-6
[6] Azevedo, B. F., Rocha, A. M. A., & Pereira, A. I. (2024). Hybrid approaches to optimization and machine learning methods: a systematic literature review. Machine Learning, 113(7), 4055-4097. https://doi.org/10.1007/s10994-023-06467-x
[7] Bansal, J. C. (2018). Particle swarm optimization. In Evolutionary and swarm intelligence algorithms (pp. 11-23). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-91341-4_2
[8] Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020). Interpretability of machine learning‐based prediction models in healthcare. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5), e1379. https://doi.org/10.1002/widm.1379
[9] Fadaee, M., & Radzi, M. A. M. (2012). Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review. Renewable and sustainable energy reviews, 16(5), 3364-3369. https://doi.org/10.1016/j.rser.2012.02.071
[10] Starke, A. R., Cardemil, J. M., Escobar, R., & Colle, S. (2018). Multi-objective optimization of hybrid CSP+ PV system using genetic algorithm. Energy, 147, 490-503. https://doi.org/10.1016/j.energy.2017.12.116
[11] Zhou, H., Sun, G., Fu, S., Liu, J., Zhou, X., & Zhou, J. (2019). A big data mining approach of PSO-based BP neural network for financial risk management with IoT. IEEE access, 7, 154035-154043. doi: 10.1109/ACCESS.2019.2948949
[12] Liu, T., & Yu, Z. (2022). The analysis of financial market risk based on machine learning and particle swarm optimization algorithm. EURASIP Journal on Wireless Communications and Networking, 2022(1), 31. https://doi.org/10.1186/s13638-022-02117-3
[13] Vivekanandan, T., & Iyengar, N. C. S. N. (2017). Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease. Computers in biology and medicine, 90, 125-136. https://doi.org/10.1016/j.compbiomed.2017.09.011
[14] Manikandan, R., Kumar, A., & Gupta, D. (2020). Hybrid computational intelligence for healthcare and disease diagnosis. In Hybrid Computational Intelligence (pp. 97-122). Academic Press. https://doi.org/10.1016/B978-0-12-818699-2.00006-8
[15] Wang, F., Li, Y., Liao, F., & Yan, H. (2020). An ensemble learning based prediction strategy for dynamic multi-objective optimization. Applied Soft Computing, 96, 106592. https://doi.org/10.1016/j.asoc.2020.106592
[16] Ouyang, H., Lin, X., Li, S., Gao, L., & Houssein, E. H. (2025). Recent metaheuristic algorithms for multi-objective feature selection: review, applications, open issues and challenges. Cluster Computing, 28(7), 467. https://doi.org/10.1007/s10586-024-04996-1
[17] Dhanka, S., Sharma, A., Kumar, A., Maini, S., & Vundavilli, H. (2025). Advancements in Hybrid Machine Learning Models for Biomedical Disease Classification Using Integration of Hyperparameter-Tuning and Feature Selection Methodologies: A Comprehensive Review. Archives of Computational Methods in Engineering, 1-36. https://doi.org/10.1007/s11831-025-10309-5
[18] David, R., Shankar, H., Kura, P., Kowtarapu, K., & Karkuzhali, S. (2025, March). Advancement in Explainable AI: Bringing Transparency and Interpretability to Machine Learning Models for Use in High-Stakes Decisions. In 2025 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 1-6). IEEE. doi: 10.1109/ESCI63694.2025.10988079
[19] Singh, Y., & Biswas, A. (2024). Adaptation of nature inspired optimization algorithms for deep learning. In Advances in Computers (Vol. 135, pp. 417-455). Elsevier. https://doi.org/10.1016/bs.adcom.2023.12.005
[20] Sanjana, N., Immanual, R., Kirthika, K. M., Sangeetha, S., & Maharaja, K. BIAs-based Deep Learning (DL) Models. In Bio-inspired Algorithms in Machine Learning and Deep Learning for Disease Detection (pp. 23-47). CRC Press. https://doi.org/10.1201/9781003538158
[21] Harkare, V., Mangrulkar, R., Thorat, O., & Jain, S. R. (2024). Evolutionary approaches for multi-objective optimization and pareto-optimal solution selection in data analytics. In Applied multi-objective optimization (pp. 67-94). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0353-1_4
[22] Giannopoulos, D., Katsikas, G., Trantzas, K., Klonidis, D., Tranoris, C., Denazis, S., ... & Burgaleta, Á. (2023, June). ACROSS: Automated zero-touch cross-layer provisioning framework for 5G and beyond vertical services. In 2023 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) (pp. 735-740). IEEE. doi: 10.1109/EuCNC/6GSummit58263.2023.10188293
[23] Khan, Z., & Ambadekar, S. (2024, June). AI-Powered Collective Decision-Making Systems and the Future Trends. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-10). IEEE. doi: 10.1109/ICCCNT61001.2024.10725853
[24] Hamid, M., Anisurrahman, & Alam, B. (2025). Quantum Machine Learning for Drug Discovery: A Systematic Review. International Journal on Computational Modelling Applications, 2(3), 01–08. https://doi.org/10.63503/j.ijcma.2025.156
[25] Sandeep Singh Sikarwar. (2025). Computation Intelligence Techniques for Security in IoT Devices. International Journal on Computational Modelling Applications, 2(1), 15–27. https://doi.org/10.63503/j.ijcma.2025.48
