Investigation of the efficiency of evolutionary methods in high-dimensional discrete optimization problems

In many applied fields, the challenge of making optimal decisions is frequently transformed into discrete optimization problems. A common approach to solving such problems involves the use of evolutionary algorithms. While these methods have proven to be effective, they demand careful adjustment of parameters for each particular task and are usually examined separately, without exploring possibilities for their cooperative use or dynamic interchange. Moreover, existing studies have been limited to relatively low-dimensional problems, which has hindered the evaluation of algorithm scalability in real-world large-scale tasks (involving up to thousands of variables). This article aims to refine the set of effective configurations for evolutionary algorithms to optimize the performance of a developed intelligent algorithm-switching system. A comparative analysis of configurations for four classes of evolutionary algorithms – genetic, ant colony, bee colony, and simulated annealing – was conducted. Experiments were performed on high-dimensional test problems (up to 20000 points). The primary research methods included comparison and grouping of results, as well as analysis of computational experiment series to assess algorithm scalability and robustness against the "curse of dimensionality". In prior experiments with low-dimensional problems, differences in algorithm configurations were barely noticeable, whereas significant performance disparities emerged in high-dimensional tasks. As a result, optimal configurations for each algorithm class were identified. The findings hold practical value for developing automated decision-support systems in logistics, manufacturing, and other engineering applications requiring reliable and scalable optimization tools.

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