Sequential Stopping Rules for Random Optimization Methods with Applications to Multistart Local Search
Sequential Stopping Rules for Random Optimization Methods with Applications to Multistart Local Search
复制标题
随机优化方法的顺序停止规则及其在多启动本地搜索中的应用
DOI：
10.1137/s1052623494277317
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
W. Hart
中科院分区：
文献类型：
--
作者：
W. Hart
描述了几种估计函数全局最小值的随机算法的顺序停止规则。描述了纯随机搜索和分层随机搜索的停止规则。这些停止规则使用 $\epsilon$ 关闭点的概率度量估计来在达到指定置信度时终止这些算法。数值结果表明，这些停止规则需要更少的样本，并且比这些算法之前的停止规则更可靠。这些停止规则也可以应用于多起点局部搜索和分层多起点局部搜索。标准测试集上的数值结果表明，这些停止规则可以与多起点本地搜索的贝叶斯停止规则一样执行。
Sequential stopping rules are described for several stochastic algorithms that estimate the global minimum of a function. Stopping rules are described for pure random search and stratified random search. These stopping rules use an estimate of the probability measure of the $\epsilon$-close points to terminate these algorithms when a specified confidence has been achieved. Numerical results indicate that these stopping rules require fewer samples and are more reliable than the previous stopping rules for these algorithms. These stopping rules can also be applied to multistart local search and stratified multistart local search. Numerical results on a standard test set show that these stopping rules can perform as well as Bayesian stopping rules for multistart local search.