A problem decomposition approach for large-scale global optimization problems

International Workshop "Advanced Technologies in Material Science, Mechanical and Automation Engineering – MIP: Engineering – 2019"

材料科学;机械制造;原子能学

Vakhnin, A.V.^1 ; Sopov, E.A.^1^2 ; Panfilov, I.A.^1^2 ; Polyakova, A.S.^1 ; Kustov, D.V.^2

Reshetnev Siberian State University of Science and Technology, Krasnoyarskiy Rabochiy av. 31, Krasnoyarsk

660037, Russia^1

Siberian Federal University, 79 Svobodny pr., Krasnoyarsk

660041, Russia^2

关键词: Cooperative co-evolution;    Large scale global optimizations;    Meta heuristic algorithm;    Objective functions;    Optimization problems;    Problem decomposition;    Real-world optimization;    State-of-the-art algorithms;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/537/5/052031/pdf
DOI  :  10.1088/1757-899X/537/5/052031

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

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.