Surrogate-based optimization for mixed-integer nonlinear problems
Surrogate-based optimization for mixed-integer nonlinear problems
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DOI：
10.1016/j.compchemeng.2020.106847
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Sun Hye Kim;Fani Boukouvala
中科院分区：
文献类型：
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作者：
Sun Hye Kim;Fani Boukouvala
使用代理模型的基于仿真的优化通过交换来自高保真模型的数据和近似值的开发来实现决策。许多化学工程优化问题，如过程设计和合成，依赖于模拟，并包含离散和连续的决策变量。基于代理的连续变量优化已经得到了广泛的研究，然而，有许多开放的挑战的情况下，混合变量的输入。在这项工作中，我们提出了一种算法的混合整数非线性模拟为基础的问题，使用自适应采样和替代建模与独热编码。我们提出了混合变量问题的实验设计技术，混合变量响应面的替代建模，以及迭代近似优化过程，导致最优解。结果表明，独热编码导致准确和强大的混合变量高斯过程和神经网络模型，是有效的替代优化。混合整数非线性基准问题和化工过程综合的案例研究所提出的算法进行了测试。
Simulation-based optimization using surrogate models enables decision-making through the exchange of data from high-fidelity models and development of approximations. Many chemical engineering optimization problems, such as process design and synthesis, rely on simulations and contain both discrete and continuous decision variables. Surrogate-based optimization with continuous variables has been studied extensively; however, there are many open challenges for the case of mixed-variable inputs. In this work, we propose an algorithm for mixed-integer nonlinear simulation-based problems that uses adaptive sampling and surrogate modeling with one-hot encoding. We propose techniques for the design of experiments for mixed-variable problems, surrogate modeling for mixed-variable response surfaces, and iterative approximation-optimization procedure that leads to optimal solutions. Results show that one-hot encoding leads to accurate and robust mixed-variable Gaussian Process and Neural Network models that are effective surrogates for optimization. The proposed algorithm is tested on mixed-integer nonlinear benchmark problems and a chemical process synthesis case study.