This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity (HF) and low-fidelity (LF)) models are available. The HF model is expensive and accurate while the LF model is cheaper to compute but less accurate. To exploit the correlation between the LF and HF models and improve algorithm efficiency, in MRSO, we first apply the DYCORS (dynamic coordinate search algorithm using response surface) algorithm to search on the LF model and then employ a potential area detection procedure to identify the promising points from the LF model. The promising points serve as the initial start points when we further search for the optimal solution based on the HF model. The performance of MRSO is compared with 6 other surrogate-based optimization methods (4 are using a single-fidelity surrogate and the rest 2 are using multi-fidelity surrogates). The comparisons are conducted on a multi-fidelity optimization test suite containing 10 problems with 10 and 30 dimensions. Besides the benchmark functions, we also apply the proposed algorithm to a practical and computationally expensive capacity planning problem in manufacturing systems which involves discrete event simulations. The experimental results demonstrate that MRSO outperforms all the compared methods.

针对多保真（高保真（HF）和低保真（LF））模型，本文提出了一种基于多保真RBF（径向基函数）替代的优化框架（MRSO）。可用。HF模型既昂贵又准确，而LF模型则便宜，但准确性较低。为了利用LF模型和HF模型之间的相关性并提高算法效率，在MRSO中，我们首先应用DYCORS（使用响应面的动态坐标搜索算法）算法搜索LF模型，然后采用潜在的区域检测程序来识别LF模型的有希望的观点。当我们进一步基于HF模型寻找最优解时，有希望的点将作为初始起点。将MRSO的性能与其他6种基于替代的优化方法进行了比较（4种使用单保真替代，其余2种使用多保真替代）。比较是在包含10个维度和30个维度的10个问题的多保真度优化测试套件上进行的。除基准功能外，我们还将提出的算法应用于涉及离散事件模拟的制造系统中的实际操作和计算量大的产能计划问题。实验结果表明，MRSO优于所有比较方法。除基准功能外，我们还将提出的算法应用于涉及离散事件模拟的制造系统中的实际操作和计算量大的产能计划问题。实验结果表明，MRSO优于所有比较方法。除基准功能外，我们还将提出的算法应用于涉及离散事件模拟的制造系统中的实际操作和计算量大的产能计划问题。实验结果表明，MRSO优于所有比较方法。