A novel global search algorithm for nonlinear mixed-effects models using particle swarm optimization

Seongho Kim, Lang Li

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

NONMEM is one of the most popular approaches to a population pharmacokinetics/pharmacodynamics (PK/PD) analysis in fitting nonlinear mixed-effects models. As a local optimization algorithm, NONMEM usually requires an initial value close enough to the global optimum. This paper proposes a novel global search algorithm called P-NONMEM. It combines the global search strategy by particle swarm optimization (PSO) and the local estimation strategy of NONMEM. In the proposed algorithm, initial values (particles) are generated randomly by PSO, and NONMEM is implemented for each particle to find a local optimum for fixed effects and variance parameters. P-NONMEM guarantees the global optimization for fixed effects and variance parameters. Under certain regularity conditions, it also leads to global optimization for random effects. Because P-NONMEM doesn't run PSO search for random effect estimation, it avoids tremendous computational burden. In the simulation studies, we have shown that P-NONMEM has much improved convergence performance than NONMEM. Even when the initial values were far away from the global optima, P-NONMEM converged nicely for all fixed effects, random effects, and variance components.

Original languageEnglish (US)
Pages (from-to)471-495
Number of pages25
JournalJournal of Pharmacokinetics and Pharmacodynamics
Volume38
Issue number4
DOIs
StatePublished - Aug 1 2011

Keywords

  • Global optimization
  • Nonlinear mixed-effects model
  • Particle swarm optimization

ASJC Scopus subject areas

  • Pharmacology

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