A Hybridization of Local and Global Search for Dynamic Multi-Objective Optimization Problem : доклад, тезисы доклада

Rurich Maria; Sherstnev Pavel

Дополнительные ссылки: eLibrary · Источник статьи

База данных: Каталог библиотеки СФУ

Библиографическое описание: A Hybridization of Local and Global Search for Dynamic Multi-Objective Optimization Problem : доклад, тезисы доклада / M. M. Rurich, P. P. Sherstnev. - [S. l. : s. n.], 2022. - Текст : непосредственный // HYBRID METHODS OF MODELING AND OPTIMIZATION IN COMPLEX SYSTEMS : Proceedings of the International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022) / Krasnoyarsk Regional Science and Technology City Hall, Russia Siberian Federal University, Reshetnev Siberian State University of Science and Technology ; International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022) (2022 ; 22.11 - 24.11 ; Krasnoyarsk). - London, United Kingdom : European Proceedings, 2022. - P. 321-327. - ISBN 9781802969603, DOI 10.15405/epct.23021.39.

Аннотация: Dynamic multi-objective optimization problems are challenging and currently not-well understood class of optimization problems but it is important since many real-world optimization problems change over time. When changes appear in the problem, there is a necessity to adapt to the changes in such a way that the convergence rate is sufficiently high. The work is devoted to the analysis of the efficiency of Pareto local search algorithms for dynamic multi-objective optimization problems as a method to increase the convergence rate. We propose a hybrid of global and local search algorithms, we use NSGA-2 algorithm as a global optimizer and L-BFGS-B as a local search algorithm. The IEEE CEC2018 benchmark set is used for the experimental comparison of investigated approaches. The experimental results show that the proposed hybridization of NSGA-2 and a local search algorithm can efficiently identify the Pareto front in the case of not intense changes in the environment.

Год издания: 2022

Авторы: Rurich Maria , Sherstnev Pavel

Издательство: European Proceedings, London, United Kingdom

Источник: HYBRID METHODS OF MODELING AND OPTIMIZATION IN COMPLEX SYSTEMS

Номера страниц: 321-327

Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology, Siberian federal university

Мероприятие: International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022) (Krasnoyarsk, 2022 ; 22.11 - 24.11)

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A Hybridization of Local and Global Search for Dynamic Multi-Objective Optimization Problem : доклад, тезисы доклада
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