ARTIFICIAL INTELLIGENCE AND DECISION MAKINGARTIFICIAL INTELLIGENCE AND DECISION MAKINGИскусственный интеллект и принятие решений2071-859426543010.14357/20718594240207VQYBODSystem, Evolutionary, Cognitive ModelingСистемное, эволюционное, когнитивное моделированиеResearch ArticleHybrid algorithm for mixed multi-objective optimization «cuckoo search» with genetic crossover operatorГибридный алгоритм смешанной многокритериальной оптимизации «кукушкин поиск» с генетическим оператором скрещиванияSarinKonstantin S.СаринКонстантин Сергеевич
Russian Federation

Candidate of technical sciences, docent, Assistant Professor, Senior Researcher

кандидат технических наук, доцент, старший научный сотрудник

sarin.konstantin@mail.ruTomsk State University of Control Systems and RadioelectronicsТомский государственный университет систем управления и радиоэлектроники050820242871050310202403102024Copyright ©; 2024, ФИЦ ИУ РАНCopyright ©; 2024,2024ФИЦ ИУ РАНhttps://journals.rcsi.science/2071-8594/article/view/265430

The article proposes a mixed-integer multi-objective optimization algorithm based on the cuckoo search metaheuristic and the genetic crossover operator. Search in discrete space is carried out using a genetic operator, and in continuous space using a metaheuristic strategy. Performance was as- sessed using modified ZDT and DTLZ tests with mixed variables. The experimental results showed the high efficiency of the proposed algorithm on complex estimates of convergence and diversity.

В статье предлагается многокритериальный алгоритм смешанной оптимизации, основанный на метаэвристике «кукушкин поиск» и генетическом операторе скрещивания. Поиск в дискретном пространстве осуществляется с помощью генетического оператора, а в непрерывном пространстве - с помощью стратегии метаэвристики. Работоспособность оценивалась на модифицированных тестах ZDT и DTLZ со смешанными переменными. Результаты эксперимента показали высокую эффективность предлагаемого алгоритма на комплексных оценках сходимости и многообразия.

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