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Efficient Generalized Surrogate-Assisted Evolutionary Algorithm for High-Dimensional Expensive Problems

delete2020-04-01
delete109
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蔡习文 cover
蔡习文 (Xiwen Cai)
L
Liang Gao *
X
Xinyu Li
DOI：10.1109/TEVC.2019.2919762delete
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Abstract

Abstract

En 中文
Engineering optimization problems usually involve computationally expensive simulations and many design variables. Solving such problems in an efficient manner is still a major challenge. In this paper, a generalized surrogate-assisted evolutionary algorithm is proposed to solve such high-dimensional expensive problems. The proposed algorithm is based on the optimization framework of the genetic algorithm (GA). This algorithm proposes to use a surrogate-based trust region local search method, a surrogate-guided GA (SGA) updating mechanism with a neighbor region partition strategy and a prescreening strategy based on the expected improvement infilling criterion of a simplified Kriging in the optimization process. The SGA updating mechanism is a special characteristic of the proposed algorithm. This mechanism makes a fusion between surrogates and the evolutionary algorithm. The neighbor region partition strategy effectively retains the diversity of the population. Moreover, multiple surrogates used in the SGA updating mechanism make the proposed algorithm optimize robustly. The proposed algorithm is validated by testing several high-dimensional numerical benchmark problems with dimensions varying from 30 to 100, and an overall comparison is made between the proposed algorithm and other optimization algorithms. The results show that the proposed algorithm is very efficient and promising for optimizing high-dimensional expensive problems.
Keywords:
Optimization
Genetic algorithms
Evolutionary computation
Computational modeling
Prediction algorithms
Partitioning algorithms
Search methods
High-dimensional expensive problems
multiple surrogates
prescreening strategy
simplified Kriging
surrogate-assisted evolutionary algorithm
surrogate-guided crossover operation
trust region method
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Journal

IEEE Transactions on Evolutionary Computation cover
IEEE Transactions on Evolutionary Computation
IF:
12
Papers:
1.8K
Citations:
2.4W

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