Intellectual Technologies on Transport Intellectual Technologies on Transport Интеллектуальные технологии на транспорте 2413-2527 81591 10.20295/2413-2527-2024-137-5-11 Искусственный интеллект и машинное обучение Artificial intelligence and machine learning Искусственный интеллект и машинное обучение Optimizing Industry Trade-Off Problems in Big Data Management Using Evolutionary Algorithms: A Comparative Study Оптимизация отраслевых задач в управлении большими данными с использованием эволюционных алгоритмов: сравнительное исследование Аббас Ахмед Худайр Abbas Ahmed H. dr.ahmed.k.abbas@uodiyala.edu.iq

кандидат технических наук;

candidate of technical sciences;

 Университет Диялы Баакуба Ирак Diyala University Baquba Iraq 14 04 2024 14 04 2024 1 5 11 10 04 2024 https://atjournal.ru/en/nauka/article/81591/view

В статье предлагается новый подход к решению проблем управления большими промышленными данными с использованием генетических алгоритмов, оптимизации роя частиц, муравьиных алгоритмов и культурных алгоритмов. Исследование направлено на эффективное распределение ресурсов, балансирование противоречивых целей, таких как минимизация затрат, использование ресурсов и улучшение качества. Данный подход предлагает комплексную структуру, которая сочетает в себе преимущества различных методов оптимизации, предоставляя лицам, принимающим решения, важные сведения об оптимальных стратегиях работы с большими данными в своих отраслях. Результаты исследования показывают эффективность гибридного подхода в достижении оптимальных решений, что повышает операционную эффективность и принятие стратегических решений в эпоху больших данных.

This paper proposes a novel approach to solve complex industrial big data management problems using genetic algorithms (GA), particle swarm optimization (PSO), ant algorithms (ACO) and cultural algorithms (CA). The research aims at efficient resource allocation, balancing conflicting objectives such as cost minimization, resource utilization and quality improvement. The proposed approach offers a comprehensive framework that combines the advantages of different optimization techniques, providing decision makers with important insights into optimal big data strategies in their industries. The results of the study show the effectiveness of the hybrid approach in achieving optimal decisions, which improves operational efficiency and strategic decision making in the era of big data.

 большие данные муравьиный алгоритм культурные алгоритмы генетический алгоритм оптимизация роя частиц bigdata ant colony optimization cultural algorithms genetic algorithm particle swarm optimization

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