Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022); Krasnoyarsk; Krasnoyarsk
Год издания: 2022
Идентификатор DOI: 10.15405/epct.23021.39
Ключевые слова: Dynamic multi-objective optimization problem, pareto local search, NSGA-2
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 Показать полностьюsufficiently 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.
Журнал: HYBRID METHODS OF MODELING AND OPTIMIZATION IN COMPLEX SYSTEMS
Номера страниц: 321-327
Место издания: London, United Kingdom
Издатель: European Proceedings