Balancing Exploitation and Exploration in Discrete Optimization via Simulation Through a Gaussian Process-Based Search
Balancing Exploitation and Exploration in Discrete Optimization via Simulation Through a Gaussian Process-Based Search
复制标题
通过基于高斯过程的搜索进行模拟，平衡离散优化中的开发和探索
DOI：
10.1287/opre.2014.1315
复制
发表时间：
2014-12
影响因子：
2.7
通讯作者：
Hu Zhaolin
中科院分区：
文献类型：
--
作者：
Sun Lihua;L. Jeff Hong;Hu Zhaolin
随机搜索算法常用于求解离散的模拟优化DOVS问题。随机搜索算法最关键的组成部分是用于指导搜索工作量分配的抽样分布。一个好的抽样分布可以平衡用于搜索当前最优解的努力(称为开发)和用于搜索大部分未知区域的努力(称为探索)之间的权衡。然而，大多数DOVS问题的随机搜索算法都很难无缝地平衡这种权衡。在这篇文章中，我们提出了一种新的方案，它根据先前评估的解从快速拟合的高斯过程中推导出采样分布。结果表明，抽样分布具有理想的性质，能够自动平衡开采与勘探之间的权衡。此外，我们将这种抽样分布集成到一种称为基于高斯过程的搜索GPS的随机搜索算法中，并证明了当仿真努力达到无穷大时，该GPS算法具有期望的全局收敛。我们通过大量的数值实验说明了该算法的性质。
Random search algorithms are often used to solve discrete optimization-via-simulation DOvS problems. The most critical component of a random search algorithm is the sampling distribution that is used to guide the allocation of the search effort. A good sampling distribution can balance the trade-off between the effort used in searching around the current best solution which is called exploitation and the effort used in searching largely unknown regions which is called exploration. However, most of the random search algorithms for DOvS problems have difficulties in balancing this trade-off in a seamless way. In this paper we propose a new scheme that derives a sampling distribution from a fast fitted Gaussian process based on previously evaluated solutions. We show that the sampling distribution has the desired properties and can automatically balance the exploitation and exploration trade-off. Furthermore, we integrate this sampling distribution into a random research algorithm, called a Gaussian process-based search GPS and show that the GPS algorithm has the desired global convergence as the simulation effort goes to infinity. We illustrate the properties of the algorithm through a number of numerical experiments.
登录
查看更多内容
DOI：
10.2307/1269971
发表时间：
1982
期刊：
--
影响因子：
--
作者：
Fourth Edition;Averill M. Law
通讯作者：
Fourth Edition;Averill M. Law
DOI：
10.1145/1276927.1276932
发表时间：
2007-09
期刊：
ACM Trans. Model. Comput. Simul.
影响因子：
--
作者：
L. Hong;Barry L. Nelson
通讯作者：
L. Hong;Barry L. Nelson
影响因子：
2.5
作者：
J. Wisnowski
通讯作者：
J. Wisnowski
影响因子：
2.2
作者：
D. Yan;H. Mukai
通讯作者：
D. Yan;H. Mukai
DOI：
--
发表时间：
2002
期刊：
--
影响因子：
--
作者：
M. Sasena
通讯作者：
M. Sasena