A problem decomposition approach for large-scale global optimization problems

Описание

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Scientific Workshop «MIP Engineering 2019: Advanced Technologies in Material Science, Mechanical and Automation Engineering»; Krasnoyarsk; Krasnoyarsk

Год издания: 2019

Аннотация: In fact, many modern real-world optimization problems have the great number of variables (more than 1000), which values should be optimized. These problems have been titled as large-scale global optimization (LSGO) problems. Typical LSGO problems can be formulated as the global optimization of a continuous objective function presenПоказать полностьюted by a computational model of «Black-Box» (BB) type. For the BB optimization problem one can request only input and output values. LSGO problems are the challenge for the majority of evolutionary and metaheuristic algorithms. In this paper, we have described details on a new DECC-RAG algorithm based on a random adaptive grouping (RAG) algorithm for the cooperative coevolution framework and the well-known SaNSDE algorithm. We have tuned the number of subcomponents for RAG algorithm and have demonstrated that the proposed DECC-RAG algorithm outperforms some state-of-the-art algorithms with benchmark problems taken from the IEEE CEC'2010 and CEC'2013 competitions on LSGO.

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Издание

Журнал: IOP Conference Series: Materials Science and Engineering

Выпуск журнала: 537

Номера страниц: 52031

Издатель: Institute of Physics and IOP Publishing Limited

Авторы

  • Vakhnin A V (Reshetnev Siberian State University of Science and Technology)
  • Sopov E A (Siberian Federal University)
  • Panfilov I A (Siberian Federal University)
  • Polyakova A S (Reshetnev Siberian State University of Science and Technology)
  • Kustov D V (Siberian Federal University)

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