The World Algorithm: A Universal Metaheuristic for Complex Optimization Problems of Actinomycin Production

Authors

  • Homeira Saheli * Department of Computer Engineering, Shahrood University of Technology (SUT), Shahrood, Iran. https://orcid.org/0009-0001-2627-1445
  • Samira Saheli Department of Medical Sciences, Zahedan Branch, Islamic Azad University (IAU), Zahedan, Iran.

https://doi.org/10.48313/maa.v2i2.41

Abstract

Optimizing microbial fermentation for bioactive compound production such as Actinomycin V remains a nonlinear and multi-objective challenge. This research introduces a hybrid Artificial Neural Network (ANN), World Algorithm (WA) framework for modeling and optimizing the medium composition used in Streptomyces triostinicus fermentation. Conventional statistical models like Response Surface Methodology (RSM) struggle with nonlinear biochemical relationships. By contrast, the ANN–World hybrid leverages the predictive adaptability of neural networks and the global optimization dynamics of the WA, inspired by cooperative interactions among forest ecosystems. Experimental data generated via Central Composite Design (CCD) were used to train the ANN, while WA refined the solution space to discover optimal nutrient ratios. The optimized configuration raised the Actinomycin V yield from 110 mg/L to 458 mg/L, representing more than a fourfold enhancement. This hybrid framework provides a scalable and interpretable AI-driven methodology for the intelligent design of bioprocesses.

Keywords:

World algorithm, Optimization, Actinomycin V, Neural network, Fermentation optimization, Metaheuristic, Machine learning

References

  1. [1] Lamtar-Gholipoor, M., Fakheri, S., & Alimoradi, M. (2024). Artificial neural network TSR for optimization of actinomycin production. Big data and computing visions, 4(1), 57–66. https://doi.org/10.22105/bdcv.2024.474793.1184
  2. [2] Fakheri, S., Alimoradi, M., & Yamaghani, M. R. (2024). Colour image multilevel thresholding segmentation using trees social relationship algorithm. https://doi.org/10.21203/rs.3.rs-4479475/v1
  3. [3] Daliri, A., Asghari, A., Azgomi, H., & Alimoradi, M. (2022). The water optimization algorithm: A novel metaheuristic for solving optimization problems. Applied intelligence, 52(15), 17990–18029. https://doi.org/10.1007/s10489-022-03397-4
  4. [4] Chaudhary, U., Birbaumer, N., & Curado, M. R. (2015). Brain-machine interface (BMI) in paralysis. Annals of physical and rehabilitation medicine, 58(1), 9–13. https://doi.org/10.1016/j.rehab.2014.11.002
  5. [5] Altaheri, H., Muhammad, G., Alsulaiman, M., Amin, S. U., Altuwaijri, G. A., Abdul, W., … & Faisal, M. (2023). Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review. Neural computing and applications, 35(20), 14681–14722. https://doi.org/10.1007/s00521-021-06352-5
  6. [6] Khademi, Z., Ebrahimi, F., & Kordy, H. M. (2023). A review of critical challenges in MI-BCI: From conventional to deep learning methods. Journal of neuroscience methods, 383, 109736. https://doi.org/10.1016/j.jneumeth.2022.109736
  7. [7] Alimoradi, M., Azgomi, H., & Asghari, A. (2022). Trees social relations optimization algorithm: a new swarm-based metaheuristic technique to solve continuous and discrete optimization problems. Mathematics and computers in simulation, 194, 629–664. https://doi.org/10.1016/j.matcom.2021.12.010
  8. [8] Hu, L. & Zhang, Z. (Eds.). (2019). Deep Learning. In EEG signal processing and feature extraction (pp. 325–333). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-13-9113-2_16
  9. [9] Alimoradi, M., Zabihimayvan, M., Daliri, A., Sledzik, R., & Sadeghi, R. (2022). Deep neural classification of darknet traffic. In Artificial intelligence research and development (pp. 105–114). IOS press. https://doi.org/10.3233/FAIA220323
  10. [10] Daliri, A., Alimoradi, M., Zabihimayvan, M., & Sadeghi, R. (2024). World hyper-heuristic: A novel reinforcement learning approach for dynamic exploration and exploitation. Expert systems with applications, 244, 122931. https://doi.org/10.1016/j.eswa.2023.122931
  11. [11] Bhuvaneshwari, M., Kanaga, E. G. M., Anitha, J., Raimond, K., & George, S. T. (2021). A comprehensive review on deep learning techniques for a BCI-based communication system. In Demystifying big data, machine learning, and deep learning for healthcare analytics (pp. 131–157). Academic Press. https://doi.org/10.1016/B978-0-12-821633-0.00013-1
  12. [12] Asghari, A., Zeinalabedinmalekmian, M., Azgomi, H., Alimoradi, M., & Ghaziantafrishi, S. (2025). Farmer ants optimization algorithm: a novel metaheuristic for solving discrete optimization problems. Information, 16(3), 207. https://doi.org/10.3390/info16030207
  13. [13] Tremmel, C., Fernandez-Vargas, J., Stamos, D., Cinel, C., Pontil, M., Citi, L., & Poli, R. (2022). A meta-learning BCI for estimating decision confidence. Journal of neural engineering, 19(4), 46009. https://doi.org/10.1088/1741-2552/ac7ba8
  14. [14] Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain computer interfaces, a review. Sensors, 12(2), 1211–1279. https://doi.org/10.3390/s120201211
  15. [15] Li, D., Ortega, P., Wei, X., & Faisal, A. (2021). Model-agnostic meta-learning for EEG motor imagery decoding in brain-computer-interfacing. https://doi.org/10.48550/arXiv.2103.08664
  16. [16] Hofmann, U. G., & Stieglitz, T. (2024). Why some BCI should still be called BMI. Nature communications, 15(1), 6207. https://doi.org/10.1038/s41467-024-50603-7
  17. [17] Vidaurre, C., Sannelli, C., Müller, K. R., & Blankertz, B. (2010). Machine-learning based co-adaptive calibration: a perspective to fight bci illiteracy. Hybrid artificial intelligence systems (pp. 413–420). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_50%0A%0A
  18. [18] Yang, X. S. (Ed.). (2015). Hybrid metaheuristic algorithms: Past, present, and future. In Recent advances in swarm intelligence and evolutionary computation (pp. 71–83). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-13826-8_4%0A%0A
  19. [19] Thompson, D. E., Quitadamo, L. R., Mainardi, L., Gao, S., Kindermans, P. J., Simeral, J. D. (2014). Performance measurement for brain-computer or brain--machine interfaces: A tutorial. Journal of neural engineering, 11(3), 35001. https://doi.org/10.1088/1741-2560/11/3/035001

Published

2025-12-23

Issue

Vol. 2 No. 2 (2025): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Saheli, H. ., & Saheli, S. (2025). The World Algorithm: A Universal Metaheuristic for Complex Optimization Problems of Actinomycin Production. Metaheuristic Algorithms With Applications, 2(2). https://doi.org/10.48313/maa.v2i2.41

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