期刊
INFORMATION SCIENCES
卷 542, 期 -, 页码 228-246出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.06.045
关键词
Evolutionary optimization; Radial basis function; Differential evolution; Surrogate model; High-dimensional expensive optimization
资金
- National Natural Science Foundation of China [51722406, 51874335, 51674280]
- Shandong Provincial Natural Science Foundation [JQ201808, ZR2019JQ21]
- Fundamental Research Funds for the Central Universities [18CX02097A]
- Major Scientific and Technological Projects of CNPC [ZD2019-183-008]
- Science and Technology Support Plan for Youth Innovation of University in Shandong Province [2019KJH002]
- National Science and Technology Major Project of China [2016ZX05025001-006]
- 111 Project [B08028]
The study proposes a novel and efficient hierarchical surrogate-assisted differential evolution (EHSDE) algorithm for high-dimensional expensive optimization problems. By balancing exploration and exploitation using a hierarchical framework and utilizing global and local surrogate models to accelerate convergence speed, the algorithm demonstrates effectiveness and efficiency on benchmark functions and production optimization problems.
Surrogate-assisted evolutionary algorithms have gained increasingly attention due to the promising search capabilities for solving computationally expensive optimization problems. However, when dealing with high-dimensional expensive optimization problems, the effectiveness of surrogate-assisted algorithms deteriorates drastically. In this paper, a novel and efficient hierarchical surrogate-assisted differential evolution (EHSDE) algorithm is proposed towards high-dimensional expensive optimization problems. To balance the exploration and exploitation during the optimization process, EHSDE utilizes a hierarchical framework. In the first phase, the best and the most uncertain offspring are identified respectively. The best offspring is prescreened by a global surrogate model which is built by using a radial basis function network with all the sample points, while the most uncertain offspring is built by the Euclidean distance between offspring and existing sample points. Subsequently, two local surrogate models, which are built by using the most promising sample points and the sample points surrounding the current best solution respectively, are utilized to accelerate the convergence speed. Moreover, experimental studies are conducted on the benchmark functions from 20D to 100D and on an oil reservoir production optimization problem. The results show that the proposed method is effective and efficient for most benchmark functions and for the production optimization problem compared with other state-of-the-art algorithms. (C) 2020 Elsevier Inc. All rights reserved.
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