Модифікація методів ройового інтелекту для задач оптимізації складних процесів, об’єктів і систем
The development of high-speed methods and algorithms for global multidimensional optimization and their modifications in various fields of science, technology, and economics is an urgent problems that involves reducing computing costs, accelerating and effectively finding solutions to such problems....
Збережено в:
| Дата: | 2025 |
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| Автори: | , , , |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
General Energy Institute of the National Academy of Sciences of Ukraine
2025
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| Теми: | |
| Онлайн доступ: | https://systemre.org/index.php/journal/article/view/907 |
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| Назва журналу: | System Research in Energy |
Репозитарії
System Research in Energy| Резюме: | The development of high-speed methods and algorithms for global multidimensional optimization and their modifications in various fields of science, technology, and economics is an urgent problems that involves reducing computing costs, accelerating and effectively finding solutions to such problems. Due to the fact that most serious problems involve the search for tens, hundreds or thousands of optimal parameters of mathematical models, the search space for these parameters grows non-linearly. Modern swarm intelligence has significant potential for application in the energy industry due to its ability to optimize and solve complex problems. With its help, it is possible to solve scientific and applied problems of optimizing energy consumption in buildings, industrial complexes and urban systems, reducing energy losses and increasing the efficiency of resource use, as well as for the construction of various elements of energy systems in general. Well-known methods and algorithms of swarm intelligence are also actively used to forecast energy production from renewable sources, such as solar and wind energy. This allows better management of energy sources and planning of their use. The relevance of modifications of methods and algorithms is due to the issues of speeding up their work when solving machine learning problems, in particular, in nonlinear regression models, classification, clustering problems, where the number of observed data can reach tens and hundreds of thousands or more. The work considers and modifies well-known effective methods and algorithms of swarm intelligence (particle swarm optimization algorithm, bee optimization algorithm, differential evolution method) for finding solutions to multidimensional extremal problems with and without restrictions, as well as problems of nonlinear regression analysis. The effectiveness of the modified methods on various classical and applied problems, which are used in the design of elements of complex objects and their systems, is demonstrated. A comparative analysis of the results of these methods was carried out. |
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