Information technologies and computer sciences

Metaheuristic Method of Evolutionary Optimization Using Immune Approaches

Authors

Ya. V. Ivanchuk
Vinnytsia National Technical University ROR
O. O. Borysuk
Vinnytsia National Technical University ROR
DOI https://doi.org/10.31649/1997-9266-2026-184-1-143-151

Keywords

optimisation objective function immune systems solution convergence cloning generation

Abstract

The article examines a metaheuristic method of evolutionary optimization based on the operating principles of artificial immune systems and aimed at solving constrained multi-criteria optimization problems. It is shown that contemporary optimization tasks are characterized by a high-dimensional search space, numerous local extrema, and the need to reconcile multiple criteria, which complicates the use of classical optimization techniques. The rationale for employing nature-inspired approaches — particularly evolutionary methods grounded in artificial immune system algorithms — is provided. These methods enable adaptive global search through mechanisms of cloning, mutation and selection. An evolutionary optimization approach based on the artificial immune system algorithm is proposed, with its operators and algorithmic framework formally defined. To evaluate the method’s effectiveness, computational experiments were conducted on benchmark problems using both binary and real-valued encodings. In the XdivK problem with binary encoding, the artificial immune system algorithm demonstrated a higher convergence rate than the genetic algorithm, attributable to its intensive local search within promising solution regions. In the optimisation of a bi-modal function with real-valued encoding, the artificial immune system algorithm consistently achieved the global optimum without becoming trapped in local maxima. It was established that, for small population sizes, the artificial immune system algorithm ensures faster convergence, whereas the genetic algorithm exhibits better scalability. The results confirm the effectiveness of the proposed approach for solving complex optimisation problems and highlight the potential for further development of immune-based methods within intelligent computational systems.

129 112

How to Cite

[1]
“Metaheuristic Method of Evolutionary Optimization Using Immune Approaches”, Вісник ВПІ, no. 1, pp. 143–151, Feb. 2026, doi: 10.31649/1997-9266-2026-184-1-143-151.

Author Biographies

Ya. V. Ivanchuk, Vinnytsia National Technical University

Dr. Sc. (Eng.), Professor, Professor of the Chair of Computer Science

O. O. Borysuk, Vinnytsia National Technical University

Post-Graduate Student of the Chair of Computer Science

References

R. D. Iskovych-Lototsky, Y. V. Ivanchuk, and Y. P. Veselovsky, “Simulation of working processes in the pyrolysis plant for waste recycling,” Eastern–European Journal of Enterprise Technologies. Engineering technological systems, vol. 1, no. 8(79), pp. 11-20, 2016. https://doi.org/10.15587/1729-4061.2016.59419 .
X. Ma, J. Yang, H. Sun, Z. Hu, and L. Wei, “Feature information prediction algorithm for dynamic multi-objective optimization problems,” European Journal of Operational Research, vol. 295, no. 3, pp. 965-981, 2021. https://doi.org/10.1016/j.ejor.2021.01.028 .
R. Kvyetnyy, and Y. Ivanchuk, Computational Methods and Algorithms, Textbook. Vinnytsya: VNTU, 2024. ISBN 978-966-641-964-7 (print); ISBN 978-617-8163-19-8 (PDF).
S. Agrawal, A. Tiwari, P. Naik, and A. Srivastava, “Improved differential evolution based on multi-armed bandit for multimodal optimization problems,” Applied Intelligence, vol. 51, no. 10, pp. 7625-7646, 2021. https://doi.org/10.1007/s10489-021-02261-1 .
R. D. Iskovych-Lototsky, Y. V. Ivanchuk, N. R. Veselovska, W. Surtel, and S. Sundetov, “Automatic system for modeling vibro-impact unloading bulk cargo on vehicles,” in Proc. SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 1080860, Oct. 1, 2018. https://doi.org/10.1117/12.2501526 .
R. Kvyetnyy, Y. Ivanchuk, A. Yarovyi, and Y. Horobets, “Algorithm for Increasing the Stability Level of Cryptosystems,” in Selected Papers of the VIII Int. Scientific Conf. “Information Technology and Implementation" (IT&I-2021), vol. 3179, pp. 293-301, 2021. [Electronic resource]. Available: https://ceur-ws.org/Vol-3179/Short_2.pdf .
В. І. Шинкаренко, і О. В. Макаров, «Конструктивно-продукційне моделювання хромосом генетичного алгоритму з закодованими алгоритмами сортування,» Проблеми програмування, № 3, с. 39-52, 2025. https://doi.org/10.15407/pp2025.03.039 .
F. Freschi, and M. Repetto, “Comparison of artificial immune systems and genetic algorithms in electrical engineering optimization,” COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, no. 25. pp. 792-811, 2006. https://doi.org/10.1108/03321640610684006 .
E. D. Ülker, and S. Ülker, “Comparison study for clonal selection algorithm and genetic algorithm,” International Journal of Computer Science & Information Technology, vol. 4, no. 4, pp. 107-118, Aug. 2012. https://doi.org/10.5121/ijcsit.2012.4410 .
G. Samigulina, and Z. Samigulina, “Development of an Approach for Multicomponent Evaluation of the Efficiency of Modified Algorithms of Artificial Immune Systems,” Procedia Computer Science, vol. 231, pp. 746-752, 2024. https://doi.org/10.1016/j.procs.2023.12.143 .
R. Iskovich-Lototsky, et al.,“Terms of the stability for the control valve of the hydraulic impulse drive of vibrating and vibro-impact machines,” Przeglad Elektrotechniczny, vol. 4, no. 19, pp. 19-23, 2019. https://doi.org/10.15199/48.2019.04.04 .
S. Agrawal, A. Tiwari, P. Naik, and A. Srivastava, “Improved differential evolution based on multi-armed bandit for multimodal optimization problems,” Applied Intelligence, vol. 51, no. 10, pp. 7625-7646, 2021. https://doi.org/10.1007/s10489-021-02261-1 .
D. Czégel, H. Giaffar, J. B. Tenenbaum, and E. Szathmáry, “Bayes and Darwin: How replicator populations implement Bayesian computations,” BioEssays, vol. 44, no. 4, article 2100255, 2022. https://doi.org/10.1002/bies.202100255 .
Y. Y. Liang, J. C. Shen, and W. Li, “Evolution of compressive mechanical properties of early hypertrophic scar during laser treatment,” Journal of Biomechanics, vol. 129, article 110783, 2021. https://doi.org/10.1016/j.jbiomech.2021.110783 .
Я. В. Іванчук, і Р. Д. Іскович-Лотоцький, Методи та засоби математичного моделювання гідравлічних вібраційних і віброударних машин, моногр. Вінниця: ВНТУ, 2023, 466 с. ISBN 978-966-641-952-4.
N. Q. K. Le, et al., “A computational framework based on ensemble deep neural networks for essential genes identification,” International Journal of Molecular Sciences, vol. 21, no. 22, pp. 1-16, 2020. https://doi.org/10.3390/ijms21239070 .
Р. Н. Квєтний, Я. В. Іванчук, І. В. Богач, О. Ю. Софина, і М. В. Барабан, Методи та алгоритми комп’ютерних обчислень. Теорія і практика, підруч. Вінниця: ВНТУ, 2023, 280 с. ISBN 978-966-641-952-4.
D. Molina, F. Herrera, J. Derrac, and S. García, “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 3-18, 2011, https://doi.org/10.1016/j.swevo.2011.02.002 .

Most read articles by the same author(s)