DOI: https://doi.org/10.32515/2664-262X.2022.6(37).2.3-16
Мультиагентні метаевристичні методи рішення задачі управління запасами
Об авторах
Є.Є. Федоров, професор, доктор технічних наук, Черкаський державний технологічний університет, м. Черкаси, Україна, e-mail: fedorovee75@ukr.net, , ORCID ID: 0000-0003-3841-7373
О.В. Нечипоренко, доцент, кандидат технічних наук, Черкаський державний технологічний університет, м. Черкаси, Україна, e-mail: olne@ukr.net, ORCID ID: 0000-0002-3954-3796
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10. Chopard, B. & Tomassini, M. (2018). An Introduction to Metaheuristics for Optimization. New York: Springer. DOI: 10.1007/978-3-319-93073-2 [in English].
11. Radosavljević, J. (2018). Metaheuristic Optimization in Power Engineering. New York: The Institution of Engineering and Technology. DOI: 10.1049/PBPO131E [in English].
12. Du, K.-L. & Swamy, M. N. S. (2016). Search and Optimization by Metaheuristics. Techniques and Algorithms Inspired by Nature. Charm: Springer. DOI: 10.1007/978-3-319-41192-7 [in English].
13. Bozorg Haddad, O., Solgi, M. & Loaiciga, H. (2017). Meta-heuristic and Evolutionary Algorithms for Engineering Optimization. Hoboken, New Jersey: Wiley & Sons. DOI: 10.1002/9781119387053 [in English].
14. Alba, E., Nakib, A. & Siarry, P. (2013). Metaheuristics for Dynamic Optimization. Berlin: Springer-Verlag. DOI: 10.1007/978-3-642-30665-5 [in English].
15. Fedorov, E., Lukashenko, V., Utkina, T., Lukashenko, A. & Rudakov K. (2019). Method For Parametric Identification Of Gaussian Mixture Model Based On Clonal Selection Algorithm. CEUR Workshop Proceedings, Vol. 2353, 41-55 [in English].
16. Grygor, O. O., Fedorov, E. E., Utkina, T. Yu., Lukashenko, A. G., Rudakov, K. S. & Harder, D. A. et al. (2019). Optimization method based on the synthesis of clonal selection and annealing simulation algorithms. Radio Electronics, Computer Science, Control, 2, 90-99. DOI: 10.15588/1607-3274-2019-2-10 [in English].
17. Spears, W. M., Green, D. T. & Spears, D. F. (2010). Biases in particle swarm optimization. International Journal of Swarm Intelligence Research, Vol. 1, 2, 34–57. DOI: 10.4018/jsir.2010040103 [in English].
18. Neshat, M., Adeli, A., Sepidnam, G., Sargolzaei, M. & Toosi A. N. (2012). A Review of Artificial Fish Swarm Optimization Methods and Applications. International Journal on Smart Sensing and Intelligent Systems, Vol. 5, 1,107–148. DOI: 10.21307/ijssis-2017-474 [in English].
Пристатейна бібліографія ГОСТ
- Cox J. F., Schleher J. G. Theory of Constraints Handbook . New York: NY, McGraw-Hill, 2010. 1175 p.
- Cluster Policy of Innovative Development of the National Economy: Integration and Infrastructure Aspects : monograph / under the editorship of professor Svitlana Smerichevska. Poznań: Wydawnictwo naukowe WSPIA, 2020. 380 p.
- Diagnostic Rule Mining Based on Artificial Immune System for a Case of Uneven Distribution of Classes in Sample / S. Subbotin, A. Oliinyk, V. Levashenko, E. Zaitseva . Communications. 2016. Vol.3. P.3-11.
- Nakib A., Talbi El-G. Metaheuristics for Medicine and Biology . Berlin: Springer-Verlag, 2017. 211 p.
- Engelbrecht A. P. Computational Intelligence: an introduction . Chichester, West Sussex: Wiley & Sons, 2007. 630 p. DOI: 10.1002/9780470512517.
- Yang X.-S. Nature-inspired Algorithms and Applied Optimization . Charm: Springer, 2018. 330 p. DOI: 10.1007/978-3-642-29694-9
- Martí R. Pardalos P. M.,. Resende M. G. C. Handbook of Heuristics . Charm: Springer, 2018. 1289 p. DOI: 10.1007/978-3-319-07124-4
- Blum C., Raidl G. R. Hybrid Metaheuristics. Powerful Tools for Optimization . Charm: Springer, 2016. 157 p. DOI: 10.1007/978-3-319-30883-8
- Yang X.-S. Optimization Techniques and Applications with Examples . Hoboken, New Jersey: Wiley & Sons, 2018. 364 p. DOI: 10.1002/9781119490616
- Chopard B., Tomassini M. An Introduction to Metaheuristics for Optimization . New York: Springer, 2018. 230 p. DOI: 10.1007/978-3-319-93073-2
- Radosavljević J. Metaheuristic Optimization in Power Engineering . New York: The Institution of Engineering and Technology, 2018. 536 p. DOI: 10.1049/PBPO131E
- Du K.-L., Swamy M. N. S. Search and Optimization by Metaheuristics. Techniques and Algorithms Inspired by Nature . Charm: Springer, 2016. 434 p. DOI: 10.1007/978-3-319-41192-7
- Bozorg Haddad O., Solgi M., Loaiciga H. Meta-heuristic and Evolutionary Algorithms for Engineering Optimization . Hoboken, New Jersey: Wiley & Sons, 2017. 293 p. DOI: 10.1002/9781119387053
- Alba E., Nakib A., Siarry P.. Metaheuristics for Dynamic Optimization . Berlin: Springer-Verlag, 2013. 398 p. DOI: 10.1007/978-3-642-30665-5
- Method For Parametric Identification Of Gaussian Mixture Model Based On Clonal Selection Algorithm / E. Fedorov, V. Lukashenko, T. Utkina, A. Lukashenko, K. Rudakov . CEUR Workshop Proceedings. 2019. Vol. 2353. P. 41-55.
- Grygor O. O. Optimization method based on the synthesis of clonal selection and annealing simulation algorithms / O. O. Grygor, E. E. Fedorov, T. Yu. Utkina, A. G. Lukashenko, K. S. Rudakov, D. A. Harder et al . Radio Electronics, Computer Science, Control. 2019. № 2. P. 90-99. DOI: 10.15588/1607-3274-2019-2-10.
- Spears W. M., Green D. T., Spears D. F. Biases in particle swarm optimization . International Journal of Swarm Intelligence Research. 2010. Vol. 1, No. 2. P. 34–57. DOI: 10.4018/jsir.2010040103
- Review of Artificial Fish Swarm Optimization Methods and Applications / M. Neshat, A. Adeli, G. Sepidnam, M. Sargolzaei, A. N. Toosi. International Journal on Smart Sensing and Intelligent Systems. 2012. Vol. 5, No. 1. P. 107–148. DOI: 10.21307/ijssis-2017-474
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