NPSA: Nonparametric Simulated Annealing for Global Optimization

TL;DR

NPSA is a novel parallel nonparametric global optimization algorithm using simulated annealing, overcoming limitations of grid search methods and effectively solving complex pharmacokinetic models.

Contribution

It introduces the first parallel nonparametric simulated annealing algorithm for global optimization, improving upon existing grid search methods.

Findings

NPSA successfully optimized a pharmacokinetics model for Voriconazole.

NPSA is guaranteed to find a global maximum unlike NPAG.

NPSA is free from curse of dimensionality issues.

Abstract

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.