I.N. Sinitsyn1, A.P. Karpenko2, M.K. Sakharov3

1 FRC "Computer Science and Control" of RAS (Moscow, Russia)

2,3 Bauman MSTU (Moscow, Russia)

Paper presents the new multi-memetic modification of the Mind Evolutionary Computation (MEC) algorithm with the incorporated landscape analysis (LA) for solving global optimization in problems complex highly available systems (HAS). The proposed landscape analysis is based on the concept of Lebesgue integral and allows one to divide objective functions into three categories. Each category suggests a usage of specific hyper-heuristics for adaptive meme selection. The new algorithm and its software tools were utilized to solve an optimal control problem for the epidemic’s propagation model, based on the SIER model with pulse vaccination. Results of the numerical experiments demonstrate a significant influence of vaccination’s start time, frequency and intensity on the maximum number of infected individuals. Results of the numerical experiments demonstrate a significant influence of vaccination’s start time, frequency and intensity on the maximum number of infected individuals. The proposed algorithm helped to find and the optimal vaccination schedule in order to minimize the number of infect-ed individuals while also maintaining the volume of the utilized vaccine at the low level.

Sinitsyn I.N., Karpenko A.P., Sakharov M.K. Software tools for analysis and synthesis of stochastic systems with high availability (XIV). Highly Available Systems. 2021. V. 17. № 3. P. 59–68. DOI: https://doi.org/10.18127/j20729472-202103-05 (in Russian)

1. Karpenko A.P. Sovremennye algoritmy poiskovoj optimizacii. Algoritmy, vdohnovlennye prirodoj. M.: Izd-vo MGTU im. N.Je. Baumana. 2014. 446 s. (in Russian).
2. Karpenko A.P. Metody optimizacii (bazovyj kurs), Baza i Generator Obrazovatel'nyh Resursov. [V Internete, 2019] MGTU im. N. Je. Baumana. http://bigor.bmstu.ru/. (in Russian).
3. Neri F., Cotta C., Moscato P. Handbook of Memetic Algorithms. Springer Berlin Heidelberg. 2011. 368 p. DOI: 10.1007/978-3-64223247-3. 
4. Karpenko A.P., Saharov M.K. Mul'timemeevaja global'naja optimizacija na osnove algoritma jevoljucii razuma. Informacionnye tehnologii. 2014. № 7. S. 23-30 (in Russian).
5. Sakharov M., Karpenko A. A New Way of Decomposing Search Domain in a Global Optimization Problem. Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17). Springer. 2018. P. 398–407. DOI:10.1007/978-3-319-68321-8_41. 
6. Sakharov M., Karpenko A. Performance Investigation of Mind Evolutionary Computation Algorithm and Some of Its Modifications. Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16). Springer. 2016. P. 475–486. DOI: 10.1007/978-3-319-33609-1_43. 
7. Sakharov M.K., Karpenko A.P. Adaptive Load Balancing in the Modified Mind Evolutionary Computation Algorithm. Supercomputing Frontiers and Innovations. 2018. V. 5. № 4. Р. 5–14. DOI: 10.14529/jsfi180401 (Date accessed 08.01.19).
8. Sokolov I.A. Budzko V.I., Sinicyn I.N. Postroenie informacionno-telekommunikacionnyh sistem vysokoj dostupnosti. Sistemy vysokoj dostupnosti. 2005 T. 1. № 1. S. 6–14 (in Russian).
9. Budzko V.I. Sistemy vysokoj dostupnosti. Ot redaktora. Sistemy vysokoj dostupnosti. 2005 T. 1. № 1. S. 4–5 (in Russian).
10. Vassilev V., Fogarty T., Miller J. “Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application,” in Advances in evolutionary computing. New York, NY. USA: Springer. 2003. Р. 3–44.
11. Muñoz M.A., Kirley M., Halgamuge S.K. Exploratory landscape analysis of continuous space optimization problems using information content. IEEE Transactions on Evolutionary Computation. 2015. V. 19(1). P. 74–87. DOI: 10.1109/TEVC.2014.2302006.
12. Saharov M.K. Issledovanie modeli kontrolja zabolevaemosti s ispol'zovaniem impul'snoj vakcinacii. Naukoemkie tehnologii i intellektual'nye sistemy 2018. M.: MGTU im. N.Je. Baumana. 2018. S. 116–120 (in Russian).