Методи й алгоритми ройового інтелекту для задач нелінійного регресійного аналізу та оптимізації складних процесів, об’єктів та систем: огляд і модифікація методів й алгоритмів

• Datum: 2024
• Hauptverfasser: Khaidurov, Vladyslav, Tatenko, Vadym, Lytovchenko, Mykyta, Tsiupii, Tamara, Zhovnovach, Tetiana
• Format: Artikel
• Sprache: English
• Veröffentlicht: General Energy Institute of the National Academy of Sciences of Ukraine 2024
• Schlagworte: оптимізація; ройовий інтелект; математичне моделювання; нелінійна регресія; складні об’єкти та системи.
• Online Zugang: https://systemre.org/index.php/journal/article/view/858
• Назва журналу: System Research in Energy

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 problem that involves reducing computing costs, accelerating, and effectively searching for solutions to such problems. Since 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. Currently, there are many modern methods and algorithms of swarm intelligence that solve today's scientific and applied problems, but they require modifications due to the large spaces of searching for optimal model parameters. Modern swarm intelligence has significant potential for application in the energy industry due to its ability to optimize and solve complex problems. It can be used 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 applied 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, and 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 obtained modifications of the well-known classic effective methods and algorithms of swarm intelligence, which are present in the work, effectively solve complex scientific and applied tasks of designing complex objects and systems. A comparative analysis of methods and algorithms will be conducted in the next study on this topic.