References
- Hamer, W. On Epidemic Disease in England – The Evidence of Variability and of Persistency of Type, Lecture III. – The Lancet, Vol. 167, 1906, No 4305, pp. 733-739.
- Kendall, D. G. Deterministic and Stochastic Epidemics in Closed Populations. – In: Proc. of 3rd Berkeley Symposium on Mathematical Statistics and Probability. Vol. IV. Contributions to Biology and Problems of Health. University of California Press, 1956, pp. 149-165.
- Campos, L. C., R. P. Cysne, A. L. Madureira, G. L. Q. Mendes. Multi-Generational SIR Modeling: Determination of Parameters, Epidemiological Forecasting, and Age-Dependent Vaccination Policies. – Infectious Disease Modelling, Vol. 6, 2021, pp. 751-765.
- Ciunkiewicz, P., W. Brooke, M. Rogers, S. Yanushkevich. Agent-Based Epidemiological Modeling of COVID-19 in Localized Environments. – Computers in Biology and Medicine, Vol. 144, 2022, Article No 105396.
- Kerkmann, D., S. Korf, K. Nguyen, D. Abele, A. Schengen, C. Gerstein, J. H. Göbbert, A. Basermann, M. J. Kühn, M. Meyer-Hermann. Agent-Based Modeling for Realistic Reproduction of Human Mobility and Contact Behavior to Evaluate Test and Isolation Strategies in Epidemic Infectious Disease Spread. – Computers in Biology and Medicine, Vol. 193, 2025, Article No 110269.
- Qiu, Z., Y. Sun, X. He, J. Wei, R. Zhou, J. Bai, S. Du. Application of Genetic Algorithm Combined with Improved SEIR Model in Predicting the Epidemic Trend of COVID-19, China. – Scientific Reports, Vol. 12, 2022, No 1, Article No 8910.
- Granados, B. G., M. C. G. Quintero, C. V. Núñez. Improved Genetic Algorithm Approach for Coordinating Decision-Making in Technological Disaster Management. – Neural Computing and Applications, Vol. 36, 2024, pp. 4503-4521.
- Haouari, M., M. Mhiri. A Particle Swarm Optimization Approach for Predicting the Number of COVID-19 Deaths. – Scientific Reports, Vol. 11, 2021, Article No 16587.
- Piotrowski, A. P., A. E. Piotrowska. Differential Evolution and Particle Swarm Optimization against COVID-19. – Artificial Intelligence Review, Vol. 55, 2022, pp. 2149-2219.
- Marzia, A., M. H. Sulaiman, A. J. Mohamad. Improved Barnacle Mating Optimizer-Based Least Squares Support Vector Machine to Predict COVID-19 Confirmed Cases with Total Vaccination. – Cybernetics and Information Technologies, Vol. 23, 2023, No 1, pp. 125-140.
- Didi, Y., A. Walha, A. Wali. Integrating Environmental Clustering to Enhance Epidemic Forecasting with Machine Learning Models. – International Journal of Cognitive Computing in Engineering, Vol. 6, 2025, pp. 628-642.
- Kennedy, J., R. Eberhart. Particle Swarm Optimization. – In: Proc. of IEEE International Conference on Neural Networks, Vol. 4, 1995, pp. 1942-1948.
- Akopov, A. S. A Clustering-Based Hybrid Particle Swarm Optimization Algorithm for Solving a Multisectoral Agent-Based Model. – Studies in Informatics and Control, Vol. 33, 2024, No 2, pp. 83-95.
- Smirnov, A. V. Method for Estimating Objective Function Landscape Convexity during Extremum Search. – Russian Technological Journal, Vol. 13, 2025, No 2, pp. 121-131.
- Herrera, F., M. Lozano, J. L. Verdega. Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis. – Artificial Intelligence Review, Vol. 12, 1998, No 4, pp. 265-319.
- Akopov, A. S., L. A. Beklaryan, M. Thakur, B. D. Verma. Parallel Multi-Agent Real-Coded Genetic Algorithm for Large-Scale Black-Box Single-Objective Optimization. – Knowledge-Based Systems, Vol. 174, 2019, pp. 103-122.
- Akopov, A. S., A. L. Beklaryan, A. A. Zhukova. Optimization of Characteristics for a Stochastic Agent-Based Model of Goods Exchange with the Use of a Parallel Hybrid Genetic Algorithm. – Cybernetics and Information Technologies, Vol. 20, 2023, No 3, pp. 45-63.
- Romasevych, Y., L. Viatcheslav, B. Ziv. Advanced PSO Algorithms Development with Combined LBEST and GBEST Neighborhood Topologies. – Cybernetics and Information Technologies, Vol. 24, 2024, No 3, pp. 59-77.
- Stoilov, T., K. Stoilova. Bi-Level Optimization of Inventory and Production. – Cybernetics and Information Technologies, Vol. 25, 2025, No 1, pp. 126-141.
- Audet, C., M. Kokkolaras. Blackbox and Derivative-Free Optimization: Theory, Algorithms and Applications. – Optimization and Engineering. Vol. 17, 2016, pp. 1-2.
- Borshchev, A. The Big Book of Simulation Modeling: Multimethod Modeling with AnyLogic. Hampton. NJ, AnyLogic North America, 2013.
- Liang, J. J., P. N. Suganthan. Dynamic Multi-Swarm Particle Swarm Optimizer. – In: Proc. of IEEE Swarm Intelligence Symposium (SIS’05), Pasadena, CA, USA, 2005, pp. 124-129.
- Mojena, R. Hierarchical Grouping Methods and Stopping Rules: An Evaluation. – The Computer Journal, Vol. 20, 1977, No 4, pp. 359-363.
- Li, X., A. Engelbrecht, M. G. Epitropakis. Benchmark Functions for CEC’2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization. – Technical Report, Evolutionary Computation and Machine Learning Group, RMIT University, Australia, 2013.
