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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">caht</journal-id><journal-title-group><journal-title xml:lang="ru">Научный вестник МГТУ ГА</journal-title><trans-title-group xml:lang="en"><trans-title>Civil Aviation High Technologies</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2079-0619</issn><issn pub-type="epub">2542-0119</issn><publisher><publisher-name>Moscow State Technical University of Civil Aviation (MSTU CA)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26467/2079-0619-2019-22-3-67-78</article-id><article-id custom-type="elpub" pub-id-type="custom">caht-1520</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>АВИАЦИОННАЯ И РАКЕТНО-КОСМИЧЕСКАЯ ТЕХНИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>AVIATION, ROCKET AND SPACE TECHNOLOGY</subject></subj-group></article-categories><title-group><article-title>МОДИФИКАЦИЯ МЕТАЭВРИСТИЧЕСКОГО МЕТОДА ФЕЙЕРВЕРКОВ ДЛЯ ЗАДАЧ МНОГОКРИТЕРИАЛЬНОЙ ОПТИМИЗАЦИИ НА ОСНОВЕ НЕДОМИНИРУЕМОЙ СОРТИРОВКИ</article-title><trans-title-group xml:lang="en"><trans-title>MODIFICATION OF FIREWORKS METHOD FOR MULTIOBJECTIVE OPTIMIZATION BASED ON NON-DOMINATED SORTING</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Пантелеев</surname><given-names>А. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Panteleev</surname><given-names>A. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Пантелеев Андрей Владимирович, доктор физико-математических наук, профессор, заведующий кафедрой математической кибернетики</p></bio><bio xml:lang="en"><p>Andrei V. Panteleev, Doctor of Physical and Mathematical Sciences, Professor, Head of Mathematics and Cybernetics Chair</p></bio><email xlink:type="simple">avpanteleev@inbox.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Крючков</surname><given-names>А. Ю.</given-names></name><name name-style="western" xml:lang="en"><surname>Krychkov</surname><given-names>A. U.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Крючков Александр Юрьевич, магистрант</p></bio><bio xml:lang="en"><p>Alexander U. Krychkov, Master Degree Student of Mathematics and Cybernetics Chair</p></bio><email xlink:type="simple">alex9x99@yandex.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Московский авиационный институт (национальный исследовательский университет)</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Moscow Aviation Institute (National Research University)</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2019</year></pub-date><pub-date pub-type="epub"><day>29</day><month>06</month><year>2019</year></pub-date><volume>22</volume><issue>3</issue><fpage>67</fpage><lpage>78</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Пантелеев А.В., Крючков А.Ю., 2019</copyright-statement><copyright-year>2019</copyright-year><copyright-holder xml:lang="ru">Пантелеев А.В., Крючков А.Ю.</copyright-holder><copyright-holder xml:lang="en">Panteleev A.V., Krychkov A.U.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://avia.mstuca.ru/jour/article/view/1520">https://avia.mstuca.ru/jour/article/view/1520</self-uri><abstract><p>В работе предлагается модификация численного метода фейерверков однокритериальной оптимизации для решения задач многокритериальной оптимизации. Метод относится к метаэвристическим алгоритмам, он не гарантирует нахождения точного решения, но может найти достаточно хорошее приближенное решение. Рассматриваются многокритериальные задачи оптимизации с числовыми критериями, имеющими одинаковую важность. Допустимое решение задачи представляется вектором из действительных чисел, значение каждой компоненты которого принадлежит определенному отрезку. Под оптимальным решением понимается решение, оптимальное по Парето. Так как точных решений, оптимальных по Парето, может быть бесконечно много, рассматривается способ нахождения приближения, состоящего из конечного числа решений, оптимальных по Парето. Модификация основана на процедуре недоминируемой сортировки, которая является основной процедурой для управления процессом поиска приближенного решения. Недоминируемая сортировка – это ранжирование решений на основе значений компонент числового вектора, полученных с помощью вычисления критериев. Каждая компонента соответствует определенному критерию, а множество решений разбивается на непересекающиеся подмножества. Первое подмножество – это решения, оптимальные по Парето, второе подмножество – это решения, оптимальные по Парето, если не учитывать первое подмножество, последнее подмножество – это решения, оптимальные по Парето, если не учитывать все предыдущие подмножества. После такого разбиения принимается решение о генерировании новых допустимых решений. Работа метода протестирована на общеизвестных задачах многокритериальной оптимизации с двумя критериями: ZDT2, LZ01. Задачи отличаются структурой расположения решений, оптимальных по Парето. Так LZ01 имеет достаточно сложную структуру решений, оптимальных по Парето. В заключении обсуждаются вопросы о дальнейшем направлении исследований и о возможности модификации метода для задач многокритериальной оптимизации с произвольными, а не параллелепипедными ограничениями.</p></abstract><trans-abstract xml:lang="en"><p>The article suggests a modification for numerical fireworks method of the single-objective optimization for solving the problem of multiobjective optimization. The method is metaheuristic. It does not guarantee finding the exact solution, but can give a good approximate result. Multiobjective optimization problem is considered with numerical criteria of equal importance. A possible solution to the problem is a vector of real numbers. Each component of the vector of a possible solution belongs to a certain segment. The optimal solution of the problem is considered a Pareto optimal solution. Because the set of Pareto optimal solutions can be infinite; we consider a method for finding an approximation consisting of a finite number of Pareto optimal solutions. The modification is based on the procedure of non-dominated sorting. It is the main procedure for solutions search. Non-dominated sorting is the ranking of decisions based on the values of the numerical vector obtained using the criteria. Solutions are divided into disjoint subsets. The first subset is the Pareto optimal solutions, the second subset is the Pareto optimal solutions if the first subset is not taken into account, and the last subset is the Pareto optimal solutions if the rest subsets are not taken into account. After such a partition, the decision is made to create new solutions. The method was tested on well-known bi-objective optimization problems: ZDT2, LZ01. Structure of the location of Pareto optimal solutions differs for the problems. LZ01 have complex structure of Pareto optimal solutions. In conclusion, the question of future research and the issue of modifying the method for problems with general constraints are discussed.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>многокритериальная оптимизация</kwd><kwd>метаэвристические методы</kwd><kwd>недоминируемая сортировка</kwd><kwd>оптимальность по Парето</kwd><kwd>теория принятия решений</kwd></kwd-group><kwd-group xml:lang="en"><kwd>multiobjective optimization</kwd><kwd>metaheuristic algorithms</kwd><kwd>non-dominated sorting</kwd><kwd>Pareto efficiency</kwd><kwd>Pareto optimality</kwd><kwd>decision making</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Arias-Montano A., Coello Coello A.C., Mezura-Montes E. Multiobjective evolutionary algorithms in aeronautical and aerospace engineering // IEEE Transactions on Evolutionary Computation. 2012. Vol. 16, Iss. 5. Pp. 662–694. 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