期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 49, 期 5, 页码 1642-1656出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2809430
关键词
Differential evolution (DE); expensive constrained optimization problems (ECOP); global search; local search; surrogate model
类别
资金
- Innovation-Driven Plan in Central South University [2018CX010]
- National Natural Science Foundation of China [61673397, 61673331]
- EU Horizon 2020 Marie Sklodowska-Curie Individual Fellowships [661327]
- Engineering and Physical Sciences Research Council of U.K. [EP/K001310/1]
- Hunan Provincial Natural Science Fund for Distinguished Young Scholars [2016JJ1018]
- Science Fund of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body [31715006]
- EPSRC [EP/K001310/1] Funding Source: UKRI
- Marie Curie Actions (MSCA) [661327] Funding Source: Marie Curie Actions (MSCA)
For expensive constrained optimization problems (ECOPs), the computation of objective function and constraints is very time-consuming. This paper proposes a novel global and local surrogate-assisted differential evolution (DE) for solving ECOPs with inequality constraints. The proposed method consists of two main phases: 1) global surrogate-assisted phase and 2) local surrogate-assisted phase. In the global surrogate-assisted phase, DE serves as the search engine to produce multiple trial vectors. Afterward, the generalized regression neural network is used to evaluate these trial vectors. In order to select the best candidate from these trial vectors, two rules are combined. The first is the feasibility rule, which at first guides the population toward the feasible region, and then toward the optimal solution. In addition, the second rule puts more emphasis on the solution with the highest predicted uncertainty, and thus alleviates the inaccuracy of the surrogates. In the local surrogate-assisted phase, the interior point method coupled with radial basis function is utilized to refine each individual in the population. During the evolution, the global surrogate-assisted phase has the capability to promptly locate the promising region and the local surrogate-assisted phase is able to speed up the convergence. Therefore, by combining these two important elements, the number of fitness evaluations can be reduced remarkably. The proposed method has been tested on numerous benchmark test functions from three test suites and two real-world cases. The experimental results demonstrate that the performance of the proposed method is better than that of other state-of-the-art methods.
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