Сибирский федеральный университет

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

Конференция: International Workshop on Advanced Technologies in Material Science, Mechanical and Automation Engineering - MIP: Engineering-2019; 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 presented 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.

Журнал: IOP Conference Series: Materials Science and Engineering

Выпуск журнала: 537

Номера страниц: 052031

Издатель: Institute of Physics Publishing

• 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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