Modern optimization methods and their application features

Objectives. The authors conduct an analytical review of available optimization methods and simulation tools to identify their key features, effectiveness, and possible applications. The aim was to form an integrated picture of modern approaches, which may facilitate decision making when selecting the most appropriate method for a particular task. The key objective was to review and classify various optimization tools, which of theoretical and practical value for developers of new models.

Methods. Scientific publications and analytical materials were retrieved from specialized databases and technical documentation libraries.

Results. The analysis and classification of existing optimization methods allowed the authors to identify their advantages, disadvantages, and application features, as well as to determine the relationship between theoretical concepts and their practical implementation. During the analysis, various optimization approaches were considered, covering both classical and modern simulation methods.

Conclusions. The importance of informed selection of optimization methods, which raise the efficiency and accuracy of simulation procedures, is highlighted. The results obtained indicate the need for further study and comparative analysis of the methods used in practice in order to establish their efficiency and applicability in various scenarios. Future research directions include experimental testing of the effectiveness of various approaches based on several models in order to determine their advantages and disadvantages for a more informed selection of the method suitable for a particular task.

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