MATSuMoTo：计算量大的黑箱全局最优化问题的MATLAB代理模型
MATSuMoTo: The MATLAB Surrogate Model Toolbox For Computationally Expensive Black-Box Global Optimization Problems
摘要：
MATSuMoTo是MATLAB的替代模型，用于计算量大、黑箱、全局最优化问题，这些问题可能具有连续、混合整数或纯整数变量。由于目标函数的黑箱性质，导数不可用。因此，代理模型被用作昂贵的目标函数的计算上便宜的近似，以指导搜索改进的解决方案。由于进行单个函数求值的计算开销，目标是在非常少的昂贵求值中找到最优解。昂贵的黑盒函数的多模态性需要一种能够在局部和全局进行搜索的算法。MatsuMoTo能够应对这些挑战。MATSUMOTO提供替代模型和替代模型混合物、初始试验设计策略和取样策略的各种选择。MATSUMOTO能够利用MATLAB的并行计算能力并行地进行多个函数的计算。
英文摘要：
MATSuMoTo is the MATLAB Surrogate Model Toolbox for computationally expensive, black-box, global optimization problems that may have continuous, mixed-integer, or pure integer variables. Due to the black-box nature of the objective function, derivatives are not available. Hence, surrogate models are used as computationally cheap approximations of the expensive objective function in order to guide the search for improved solutions. Due to the computational expense of doing a single function evaluation, the goal is to find optimal solutions within very few expensive evaluations. The multimodality of the expensive black-box function requires an algorithm that is able to search locally as well as globally. MATSuMoTo is able to address these challenges. MATSuMoTo offers various choices for surrogate models and surrogate model mixtures, initial experimental design strategies, and sampling strategies. MATSuMoTo is able to do several function evaluations in parallel by exploiting MATLAB's Parallel Computing Toolbox.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
