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A hierarchical surrogate assisted optimization algorithm using teaching-learning-based optimization and differential evolution for high-dimensional expensive problems
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DOI：10.1016/j.asoc.2023.111212.png)
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Surrogate-assisted evolutionary algorithms (SAEAs) are increasingly used in solving computationally expensive optimization problems. However, when tackling high-dimensional expensive problems, a large number of exact function evaluations (FEs) need to be consumed for existing SAEAs to achieve an acceptable solution. In this paper, a hierarchical surrogate assisted optimization algorithm (HSAOA) using teaching-learning-based optimization and differential evolution is proposed for solving high-dimensional expensive problems with a relatively small number of exact FEs. To keep a balance between global exploration and local exploitation, a hierarchical surrogate framework with hybrid evolutionary algorithms is devised. In the global search phase, a radial basis function surrogate is utilized to assist the teaching-learning-based optimization in locating the promising sub-regions. In the local search phase, a novel dynamic ensemble of surrogates is proposed to assist the differential evolution in speeding up the convergence process. Eight test functions with 10 to 100 dimensions and a spatial truss design problem are employed to compare the proposed method with several state-of-the-art SAEAs. The results show that the proposed HSAOA is superior to the comparison algorithms for solving expensive optimization problems, and needs a much smaller number of exact FEs than other competing SAEAs to produce competitive or even better results for high-dimensional expensive problems.
Keyword:
Hybrid optimization algorithm
Differential evolution
Teaching-learning-based optimization
Surrogate model
High -dimensional expensive problems
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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