Vol 30, No 12 (2024)

The traveling salesman problem is an important combinatorial optimization problem, which consists in finding the shortest path that passes through each city exactly once and ends at the starting point. Despite its simple formulation, this problem turns out to be computationally complex, and its exact solution requires significant computational resources. In this regard, the search for efficient algorithms to solve the traveling salesman problem remains an urgent task in the context of modern technological challenges. In this paper we present the results of a computational experiment that gives an insight into the quality of the cycle merging algorithm for the minimum traveling salesman problem. The quality of the cycle merging algorithm is investigated on seven sets of instances of the traveling salesman problem. The results obtained are compared with the results of computational experiments for some known heuristics. The analysis allows us to say that the cycle merging algorithm shows relatively good results for all tested sets and can be used for a wide range of instances of the traveling salesman problem as a relatively fast heuristic of good enough quality.

The design of machine learning language models, as well as their ensembles, used in complex analytics of news texts of domestic and Western electronic media is considered. An example of software implementation of a new language neural network model with problem-oriented ontological tokenization is given. The language used as tools is Python v.3.10, Anaconda v.2.1. The effectiveness of the approach in comparison with the best foreign analogues is confirmed by a series of experiments using the example of classifying news articles according to their ideological orientation into Western and English-language Russian ones.

The article examines the complex task of assessing the success potential of a startup using the ChatGPT chatbot. To solve the problem, a technique is proposed, which is based on the use of the ChatGPT language model. The effectiveness of assessing the potential for success of a startup is based on three criteria: assessing profitability, assessing the potential of startup participants and, directly, assessing the likelihood of success of the startup. Based on the methodology proposed in the article for assessing the success potential of a startup, an alpha version of a web platform based on the Laravel framework was developed. Using the developed platform, the proposed methodology was tested. The results obtained indicate the high potential of the platform for both the scientific and commercial segments.

This paper examines the combined impact of significant modern trends on competitive processes in the IT industry. These trends include industry digitalization, the use of artificial intelligence tools in software engineering, and the concept of an IT company without physical offices and with fully virtualized production processes. The determination of the level of influence of these trends on the European information technology market is based on the author’s research from 2020-2023. It is already obvious that the combination of these trends has a serious impact on the industry, but this impact continues to grow, which requires the management of IT companies to manage timely changes at the strategic level. The article offers an updated list of signs of an IT company achieving high-tech status, defines elements of a corporate strategy that allow using related advantages, and presents tactical steps for implementing timely changes. The article also presents a fundamental route for an IT company to achieve the status of a promising high-tech company with a global level of competitiveness.