In this paper we describe NPSA, the first parallel nonparametric global maximum likelihood optimization algorithm using simulated annealing (SA). Unlike the nonparametric adaptive grid search method NPAG, which is not guaranteed to find a global optimum solution, and may suffer from the curse of dimensionality, NPSA is a global optimizer and it is free from these grid related issues. We illustrate NPSA by a number of examples including a pharmacokinetics (PK) model for Voriconazole and show that NPSA may be taken as an upgrade to the current grid search based nonparametric methods.

翻译：摘要：本文描述了NPSA，这是首个采用模拟退火（SA）的并行非参数全局最大似然优化算法。与非参数自适应网格搜索方法NPAG不同，后者无法保证找到全局最优解，且可能面临维数灾难问题，而NPSA是一种全局优化器，不受网格相关问题的困扰。我们通过包括伏立康唑药代动力学（PK）模型在内的多个算例来阐述NPSA，并表明NPSA可视为对当前基于网格搜索的非参数方法的升级。