The Potential Use of Quasi-random Niederreiter Sequences in a Fireworks Algorithm
DOI:
https://doi.org/10.24160/1993-6982-2025-1-168-175Keywords:
parametric optimization, fireworks algorithm, irregular gridsAbstract
This article explores the possibility of using Niederreiter quasi-random sequences to improve the efficiency of a fireworks algorithm. The generation of «sparks» based on quasi-random sequences has a number of advantages compared with the logic conventionally used in the fireworks algorithm, since such “sparks” have a more uniform distribution of points in space, which leads to increased productivity of the optimization method. The study results may be useful for specialists in the field of optimization and artificial intelligence who are looking for new approaches to improve algorithms.
References
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18. Alba E., Sarasola B. ABC, a New Performance Tool for Algorithms Solving Dynamic Optimization Problems // Proc. IEEE Congress on Evolutionary Computation. Barcelona, 2010. Pp. 1—7.
19. Rezvanian A., Vahidipour S.M., Sadollah A. An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems // Ant Colony Optimization — Recent Variants, Application and Perspectives. Berlin: Springer, 2023.
20. de Oliveira S.M. e. a. A Computational Study on Ant Colony Optimization for the Traveling Salesman Problem with Dynamic Demands // Computers and Operations Research. 2021. V. 135. P. 105359.
---
Для цитирования: Егоров И.Н., Кретинин Г.В., Кретинин А.Г. Потенциал использования квазислучайных последовательностей Нидеррайтера в алгоритме фейерверков // Вестник МЭИ. 2025. № 1. С. 168—175. DOI: 10.24160/1993-6982-2025-1-168-175
---
Конфликт интересов: авторы заявляют об отсутствии конфликта интересов
#
1. Saymon D. Algoritmy Evolyutsionnoy Optimizatsii. M.: DMK Press, 2020. (in Russian).
2. Ying Tan. Fireworks Algorithm: a Novel Swarm Intelligence Optimization Method. Berlin: Springer, 2015.
3. Ram Kinkar Dutta, Nabin Kanti Karmakar, Tapas Si. Artificial Neural Network Training Using Fireworks Algorithm in Medical Data Mining. Intern. J. Computer Appl. 2016;137(1):975—8887.
4. La’aro Bolaji A., Aminu A.A., Bamidele Shola P. Training Of Neural Network For Pattern Classification Using Fireworks Algorithm. Intern. J. System Assurance Engineering and Management. 2018;9(1):208—215.
5. Nayak S.C. A Fireworks Algorithm Based Pi-Sigma Neural Network (FWA-PSNN) for Modelling and Forecasting Chaotic Crude Oil Price Time Series. EAI Endorsed Trans. Energy Web. 2020;7(28):162803.
6. Guo W., Guo J., Miao F. Application of Improved Process Neural Network Based on the Fireworks Algorithm in the Temperature-rise Predictions of a Large Generator Rotor. Appl. Sci. 2023;13:2943.
7. Sobol' I.M., Statnikov R.B. Vybor Optimal'nykh Parametrov v Zadachakh so Mnogimi Kriteriyami. M.: Drofa, 2006. (in Russian).
8. Tan Y., Zhu Y. Fireworks Algorithm for Optimization. Advances in Swarm Intelligence. Berlin: Springer, 2010:355—364
9. Weilin L. e. a. Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach. IEEE Access. 2020;99:1-1.
10. Chibing Gong. Dynamic Search Fireworks Algorithm with Chaos. J. Algorithms and Computational Technol. 2019;13:1—13.
11. Niederreiter H. Random Number Generators and Quasi Monte-Carlo Methods. Encyclopedia of Actuarial Sci. Philadelphia: Soc. for Industrial and Appl. Math., 1992.
12. Bratley P., Fox B.L., Niederreiter H. Implementation and Tests of Low-discrepancy Sequences. ACM Trans. Modeling and Computer Simulation. 1992;2(3):195—213.
13. Niderreiter-one API Specification [Elektron. Resurs] https://oneapi.io/spec/ (Data Obrashcheniya 20.12.2023).
14. Momin J., Xin-She Yang. A Literature Survey of Benchmark Functions for Global Optimization Problems. Intern. J. Mathematical Modelling and Numerical Optimization. 2013;4(2):150—194.
15. Kumar V. e. a. Optimal Choice of Parameters for Fireworks Algorithm. Proc. Computer Sci. 2015;70:334—340.
16. Egorov I.N., Kretinin G.V., Kretinin A.G. O Vybore Nachal'nogo Priblizheniya pri Chislennom Reshenii Zadach Parametricheskoy Optimizatsii. Izvestiya Vysshikh Uchebnykh Zavedeniy. Povolzhskiy Region. Seriya «Fiziko-Matematicheskie Nauki». 2023;1:28—39. (in Russian).
17. Egorov I.N., Fedechkin K.S., Kretinin A.G. Povyshenie Effektivnosti Protsessov Optimizatsii Osevykh Kompressorov s Ispol'zovaniem Mnogoprotsessornykh Vychislitel'nykh Sistem. Nasosy. Turbiny. Sistemy. 2021;2:24—32. (in Russian).
18. Alba E., Sarasola B. ABC, a New Performance Tool for Algorithms Solving Dynamic Optimization Problems. Proc. IEEE Congress on Evolutionary Computation. Barcelona, 2010:1—7.
19. Rezvanian A., Vahidipour S.M., Sadollah A. An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems. Ant Colony Optimization — Recent Variants, Application and Perspectives. Berlin: Springer, 2023.
20. de Oliveira S.M. e. a. A Computational Study on Ant Colony Optimization for the Traveling Salesman Problem with Dynamic Demands. Computers and Operations Research. 2021;135:105359
---
For citation: Egorov I.N., Kretinin G.V., Kretinin A.G. The Potential Use of Quasi-random Niederreiter Sequences in a Fireworks Algorithm. Bulletin of MPEI. 2025;1:168—175. (in Russian). DOI: 10.24160/1993-6982-2025-1-168-175
---
Conflict of interests: the authors declare no conflict of interest
2. Ying Tan. Fireworks Algorithm: a Novel Swarm Intelligence Optimization Method. Berlin: Springer, 2015.
3. Ram Kinkar Dutta, Nabin Kanti Karmakar, Tapas Si. Artificial Neural Network Training Using Fireworks Algorithm in Medical Data Mining // Intern. J. Computer Appl. 2016. V. 137(1). Pp. 975—8887.
4. La’aro Bolaji A., Aminu A.A., Bamidele Shola P. Training Of Neural Network For Pattern Classification Using Fireworks Algorithm // Intern. J. System Assurance Engineering and Management. 2018. V. 9(1). Pp. 208—215.
5. Nayak S.C. A Fireworks Algorithm Based Pi-Sigma Neural Network (FWA-PSNN) for Modelling and Forecasting Chaotic Crude Oil Price Time Series // EAI Endorsed Trans. Energy Web. 2020. V. 7(28). P. 162803.
6. Guo W., Guo J., Miao F. Application of Improved Process Neural Network Based on the Fireworks Algorithm in the Temperature-rise Predictions of a Large Generator Rotor // Appl. Sci. 2023. V. 13. P. 2943.
7. Соболь И.М., Статников Р.Б. Выбор оптимальных параметров в задачах со многими критериями. М.: Дрофа, 2006.
8. Tan Y., Zhu Y. Fireworks Algorithm for Optimization // Advances in Swarm Intelligence. Berlin: Springer, 2010. Pp. 355—364
9. Weilin L. e. a. Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach // IEEE Access. 2020. V. 99. P. 1-1.
10. Chibing Gong. Dynamic Search Fireworks Algorithm with Chaos // J. Algorithms and Computational Technol. 2019. V. 13. Pp. 1—13.
11. Niederreiter H. Random Number Generators and Quasi Monte-Carlo Methods // Encyclopedia of Actuarial Sci. Philadelphia: Soc. for Industrial and Appl. Math., 1992.
12. Bratley P., Fox B.L., Niederreiter H. Implementation and Tests of Low-discrepancy Sequences // ACM Trans. Modeling and Computer Simulation. 1992. V. 2(3). Pp. 195—213.
13. Niderreiter-one API Specification [Электрон. ресурс] https://oneapi.io/spec/ (дата обращения 20.12.2023).
14. Momin J., Xin-She Yang. A Literature Survey of Benchmark Functions for Global Optimization Problems // Intern. J. Mathematical Modelling and Numerical Optimization. 2013. V. 4(2). Pp. 150—194.
15. Kumar V. e. a. Optimal Choice of Parameters for Fireworks Algorithm // Proc. Computer Sci. 2015. V. 70. Pp. 334—340.
16. Егоров И.Н., Кретинин Г.В., Кретинин А.Г. О выборе начального приближения при численном решении задач параметрической оптимизации // Известия высших учебных заведений. Поволжский регион. Серия «Физико-математические науки». 2023. № 1. С. 28—39.
17. Егоров И.Н., Федечкин К.С., Кретинин А.Г. Повышение эффективности процессов оптимизации осевых компрессоров с использованием многопроцессорных вычислительных систем // Насосы. Турбины. Системы. 2021. № 2. C. 24—32.
18. Alba E., Sarasola B. ABC, a New Performance Tool for Algorithms Solving Dynamic Optimization Problems // Proc. IEEE Congress on Evolutionary Computation. Barcelona, 2010. Pp. 1—7.
19. Rezvanian A., Vahidipour S.M., Sadollah A. An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems // Ant Colony Optimization — Recent Variants, Application and Perspectives. Berlin: Springer, 2023.
20. de Oliveira S.M. e. a. A Computational Study on Ant Colony Optimization for the Traveling Salesman Problem with Dynamic Demands // Computers and Operations Research. 2021. V. 135. P. 105359.
---
Для цитирования: Егоров И.Н., Кретинин Г.В., Кретинин А.Г. Потенциал использования квазислучайных последовательностей Нидеррайтера в алгоритме фейерверков // Вестник МЭИ. 2025. № 1. С. 168—175. DOI: 10.24160/1993-6982-2025-1-168-175
---
Конфликт интересов: авторы заявляют об отсутствии конфликта интересов
#
1. Saymon D. Algoritmy Evolyutsionnoy Optimizatsii. M.: DMK Press, 2020. (in Russian).
2. Ying Tan. Fireworks Algorithm: a Novel Swarm Intelligence Optimization Method. Berlin: Springer, 2015.
3. Ram Kinkar Dutta, Nabin Kanti Karmakar, Tapas Si. Artificial Neural Network Training Using Fireworks Algorithm in Medical Data Mining. Intern. J. Computer Appl. 2016;137(1):975—8887.
4. La’aro Bolaji A., Aminu A.A., Bamidele Shola P. Training Of Neural Network For Pattern Classification Using Fireworks Algorithm. Intern. J. System Assurance Engineering and Management. 2018;9(1):208—215.
5. Nayak S.C. A Fireworks Algorithm Based Pi-Sigma Neural Network (FWA-PSNN) for Modelling and Forecasting Chaotic Crude Oil Price Time Series. EAI Endorsed Trans. Energy Web. 2020;7(28):162803.
6. Guo W., Guo J., Miao F. Application of Improved Process Neural Network Based on the Fireworks Algorithm in the Temperature-rise Predictions of a Large Generator Rotor. Appl. Sci. 2023;13:2943.
7. Sobol' I.M., Statnikov R.B. Vybor Optimal'nykh Parametrov v Zadachakh so Mnogimi Kriteriyami. M.: Drofa, 2006. (in Russian).
8. Tan Y., Zhu Y. Fireworks Algorithm for Optimization. Advances in Swarm Intelligence. Berlin: Springer, 2010:355—364
9. Weilin L. e. a. Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach. IEEE Access. 2020;99:1-1.
10. Chibing Gong. Dynamic Search Fireworks Algorithm with Chaos. J. Algorithms and Computational Technol. 2019;13:1—13.
11. Niederreiter H. Random Number Generators and Quasi Monte-Carlo Methods. Encyclopedia of Actuarial Sci. Philadelphia: Soc. for Industrial and Appl. Math., 1992.
12. Bratley P., Fox B.L., Niederreiter H. Implementation and Tests of Low-discrepancy Sequences. ACM Trans. Modeling and Computer Simulation. 1992;2(3):195—213.
13. Niderreiter-one API Specification [Elektron. Resurs] https://oneapi.io/spec/ (Data Obrashcheniya 20.12.2023).
14. Momin J., Xin-She Yang. A Literature Survey of Benchmark Functions for Global Optimization Problems. Intern. J. Mathematical Modelling and Numerical Optimization. 2013;4(2):150—194.
15. Kumar V. e. a. Optimal Choice of Parameters for Fireworks Algorithm. Proc. Computer Sci. 2015;70:334—340.
16. Egorov I.N., Kretinin G.V., Kretinin A.G. O Vybore Nachal'nogo Priblizheniya pri Chislennom Reshenii Zadach Parametricheskoy Optimizatsii. Izvestiya Vysshikh Uchebnykh Zavedeniy. Povolzhskiy Region. Seriya «Fiziko-Matematicheskie Nauki». 2023;1:28—39. (in Russian).
17. Egorov I.N., Fedechkin K.S., Kretinin A.G. Povyshenie Effektivnosti Protsessov Optimizatsii Osevykh Kompressorov s Ispol'zovaniem Mnogoprotsessornykh Vychislitel'nykh Sistem. Nasosy. Turbiny. Sistemy. 2021;2:24—32. (in Russian).
18. Alba E., Sarasola B. ABC, a New Performance Tool for Algorithms Solving Dynamic Optimization Problems. Proc. IEEE Congress on Evolutionary Computation. Barcelona, 2010:1—7.
19. Rezvanian A., Vahidipour S.M., Sadollah A. An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems. Ant Colony Optimization — Recent Variants, Application and Perspectives. Berlin: Springer, 2023.
20. de Oliveira S.M. e. a. A Computational Study on Ant Colony Optimization for the Traveling Salesman Problem with Dynamic Demands. Computers and Operations Research. 2021;135:105359
---
For citation: Egorov I.N., Kretinin G.V., Kretinin A.G. The Potential Use of Quasi-random Niederreiter Sequences in a Fireworks Algorithm. Bulletin of MPEI. 2025;1:168—175. (in Russian). DOI: 10.24160/1993-6982-2025-1-168-175
---
Conflict of interests: the authors declare no conflict of interest
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2024-06-18
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Mathematical Modeling, Numerical Methods and Program Complexes (Technical Sciences) (1.2.2)
