РОЗРОБКА АДАПТИВНИХ АЛГОРИТМІВ РОЄВОГО ІНТЕЛЕКТУ У ПРОЕКТУВАННІ ТА УПРАВЛІННІ ІНЖЕНЕРНИМИ СИСТЕМАМИ

Abstract

The article examines the design and management of engineering systems in the context of growing data volumes in local computer networks. With the development of swarm intelligence, the probability of the existence of the necessary information for managing engineering systems increases, but the possibility of finding it decreases. This creates the need to develop new methods and tools for using adaptive algorithms of swarm intelligence. The purpose of such algorithms is to ensure the ability to process information and find queries with maximum relevance, generating the largest number of search results (SERPs) containing technical information. The development of swarm intelligence algorithms is impossible without their intellectualization, which includes semantic and syntactic analysis of texts, natural language tools, and intelligent algorithms for determining the significance of information resources. In digital information retrieval systems (DIRS), a query is formed in the form of keywords or their combinations, connected by logical operations. To search for the same information, different keywords are used, the choice of which is subjective. The first stage of the algorithm consists in determining the set of meaningful correspondences and forming the initial set of requests within the engineering system. To ensure the completeness of the selection, the total relevance of the SERP in relation to the group of significance criteria is considered. There is a need to develop an efficient neural network algorithm to solve the problem of assigning one SERP to each group of query criteria. This will make it possible to distribute SERPs by criteria groups so that each page is evaluated mainly by one criteria group, and the total relevance of all groups is maximized. Thus, the article emphasizes the importance of developing new methods and tools for implementing adaptive swarm intelligence algorithms in engineering systems. Special attention is paid to the development of effective neural network algorithms that will ensure optimal distribution of the relevance of search pages, which will increase the accuracy and efficiency of information search in complex engineering systems.