kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университетkaz29-138Research ArticleФИЗИКО-МАТЕМАТИЧЕСКИЕ И ТЕХНИЧЕСКИЕ НАУКИPHYSICAL, MATHEMATICAL AND TECHNICAL SCIENCESМНОГОФУНКЦИОНАЛЬНАЯ МУЛЬТИАГЕНТНАЯ SMART-СИСТЕМА НА ОСНОВЕ МОДИФИЦИРОВАННЫХ АЛГОРИТМОВ РОЕВОГО ИНТЕЛЛЕКТАMULTIFUNCTIONAL MULTI-AGENT SMART-SYSTEM BASED ON MODIFIED SWARM INTELLIGENCE ALGORITHMSСамигулинаГ. А.SamigulinaG. A.

д. т. н., доцент

МасимкановаЖ. А.MasimkanovaZh. A.

докторант PhD, МНС

АО «Казахстанско-Британский технический университет»; Институт информационных и вычислительных технологий КН МОН РККазахстанКазахский Национальный университет им. аль-ФарабиКазахстан201907112021162157164Copyright © Самигулина Г.А., Масимканова Ж.А., 20212021Самигулина Г.А., Масимканова Ж.А.Samigulina G.A., Masimkanova Z.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/138

Статья посвящена разработке многофункциональной мультиагентной Smart-системы прогнозирования и управления сложными объектами на основе модифицированных алгоритмов роевого интеллекта и искусственных иммунных систем. Разработан модифицированный алгоритм СPSOIW-CS на основе кооперативного алгоритма роя частиц с весом инерции (СPSOIW) и алгоритма клональной селекции (CS). Предварительная обработка данных и выделение информативных дескрипторов выполняются на основе СPSOIW алгоритма. Применение СPSOIWалгоритма позволяет более детально и быстро исследовать многомерное пространство и избежать преждевременной сходимости. Распознавание образов и прогноз осуществляются с использованием CS алгоритма. В работе показана актуальность и перспективность внедрения разработанной системы в реальное производство. Разработана структурная схема многофункциональной мультиагентной Smart-системы. Созданы следующие агенты: агент базы данных, менеджер агент, агент помощник, онтологический агент, кооперативный агент роя частиц с весом инерции, агент распознавания образов и агент оценки прогнозирования, а также описаны их функции. Создано программное обеспечение MCPSO (Multi-agent Cooperative Particle Swarm Optimization) на языке программирования Python, предназначенное для обработки многомерных данных, выделения информативных дескрипторов и построения оптимального набора параметров, характеризующих объект, которое является модулем многофункциональной мультиагентной Smart-системы.

The article is devoted to the development of multifunctional multi-agent Smart-system for prediction and control of complex objects based on modified swarm intelligence algorithms and artificial immune systems. Modified CPSOIW-CS algorithm has been developed based on cooperative particle swarm optimization with inertia weight (СPSOIW) and clonal selection (CS) algorithm. Preliminary data processing and selection of informative descriptors have been performed based on CPSOIW algorithm. The application of CPSOIW algorithm allows more detailed and fast exploration of multidimensional space and avoid premature convergence. Pattern recognition and prediction have been carried out using CS algorithm. In paper relevance and prospects of the developed system in real production have been shown. The block schema of multifunctional multi-agent Smart-system has been developed. The following agents have been created: database agent, manager agent, assistant agent, ontological agent, cooperative particle swarm agent with inertia weight, pattern recognition agent and prediction evaluation agent, also their functions have been described. The MCPSO (Multi-agent Cooperative Particle Swarm Optimization) software has been developed in Python programming language to process multidimensional data, select informative descriptors and create an optimal set ofparameters characterizing an object and it is a module o f multifunctional multi-agent Smart system.

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The authors declare that there are no conflicts of interest present.