论文标题
关于实施全球优化方法，用于混合变量问题
On the implementation of a global optimization method for mixed-variable problems
论文作者
论文摘要
我们描述了在开源无衍生求解器rbfopt中实现的优化算法。该算法基于Gutmann的径向基函数方法以及Regis和Shoemaker的度量随机反应表面方法。我们提出了几种旨在概括和改进这两种算法的修改：（i）使用扩展空间来表示一元编码中的分类变量； （ii）在局部改善候选解决方案的改进阶段； （iii）插值模型，没有单向性条件，既可以帮助处理分类变量，又可以在唯一确定的模型之前启动优化； （iv）一个主工作者框架，可以并行允许异步目标函数评估。数值实验显示了这些思想的有效性。
We describe the optimization algorithm implemented in the open-source derivative-free solver RBFOpt. The algorithm is based on the radial basis function method of Gutmann and the metric stochastic response surface method of Regis and Shoemaker. We propose several modifications aimed at generalizing and improving these two algorithms: (i) the use of an extended space to represent categorical variables in unary encoding; (ii) a refinement phase to locally improve a candidate solution; (iii) interpolation models without the unisolvence condition, to both help deal with categorical variables, and initiate the optimization before a uniquely determined model is possible; (iv) a master-worker framework to allow asynchronous objective function evaluations in parallel. Numerical experiments show the effectiveness of these ideas.
