Robust optimization with simulated annealing
Robust optimization with simulated annealing
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DOI：
10.1007/s10898-009-9496-x
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发表时间：
2010-10
影响因子：
1.8
通讯作者：
D. Bertsimas;O. Nohadani
中科院分区：
文献类型：
--
作者：
D. Bertsimas;O. Nohadani
复杂的系统可以优化，以提高性能，相对于期望的功能。然而，当遇到实现或输入数据中的错误时，优化的解决方案可能变得次优甚至不可行的。我们报告了一种不需要任何问题结构知识的鲁棒模拟退火算法。这在许多工程应用中是必要的，因为解决方案往往不明确地知道，必须通过数值模拟来获得。这种非凸全局优化方法在提高算法性能和鲁棒性的同时，也保证了对数据和实现的不确定性具有鲁棒性的全局最优。我们在多项式优化问题和高维复杂纳米光子工程问题上进行了演示，并在效率和实际最优性方面取得了显着改善。
Complex systems can be optimized to improve the performance with respect to desired functionalities. An optimized solution, however, can become suboptimal or even infeasible, when errors in implementation or input data are encountered. We report on a robust simulated annealing algorithm that does not require any knowledge of the problems structure. This is necessary in many engineering applications where solutions are often not explicitly known and have to be obtained by numerical simulations. While this nonconvex and global optimization method improves the performance as well as the robustness, it also warrants for a global optimum which is robust against data and implementation uncertainties. We demonstrate it on a polynomial optimization problem and on a high-dimensional and complex nanophotonic engineering problem and show significant improvements in efficiency as well as in actual optimality.