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A bi-fidelity Bayesian optimization method for multi-objective optimization with a novel acquisition function
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AI
DOI：10.1007/s00158-023-03509-9.png)
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Abstract
En 中文
In engineering design optimization, there are often multiple conflicting optimization objectives. Bayesian optimization (BO) is successfully applied in solving multi-objective optimization problems to reduce computational expense. However, the expensive expense associated with high-fidelity simulations has not been fully addressed. Combining the BO methods with the bi-fidelity surrogate model can further reduce expense by using the information of samples with different fidelities. In this paper, a bi-fidelity BO method for multi-objective optimization based on lower confidence bound function and the hierarchical Kriging model is proposed. In the proposed method, a novel bi-fidelity acquisition function is developed to guide the optimization process, in which a cost coefficient is adopted to balance the sampling cost and the information provided by the new sample. The proposed method quantifies the effect of samples with different fidelities for improving the quality of the Pareto set and fills the blank of the research domain in extending BO based on the lower confidence bound (LCB) function with bi-fidelity surrogate model for multi-objective optimization. Compared with the four state-of-the-art BO methods, the results show that the proposed method is able to obviously reduce the expense while obtaining high-quality Pareto solutions.
Keywords:
Bayesian optimization
Bi-fidelity surrogate model
Lower confidence bounds function
Multi-objective optimization
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