LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models

Xinliang Tian, Wei Pang, Yizhang Wang, Kaimin Guo, You Zhou (Corresponding Author)

Research output: Contribution to journalArticle


Simultaneously inferring both the structure and parameters of Ordinary Differential Equations (ODEs) for a complex dynamic system is more practical in many systems identification problems, but it remains challenging due to the complexity of the underlying search space. In this research, we propose a novel algorithm based on Particle Swarm Optimization (PSO) and Latin Hypercube Sampling (LHS) to address the above problem. The proposed algorithm is termed LatinPSO, and it can be effectively used for inferring the structure and parameters of ODE models through time course data. To start with, the real Human Immunodeficiency Virus (HIV) model and several synthetic models are used for evaluating the performance of LatinPSO. Experimental results demonstrated that LatinPSO could find satisfactory candidate ODE models with appropriate structure and parameters.
Original languageEnglish
Pages (from-to)8-16
Number of pages9
Early online date2 Jun 2019
Publication statusPublished - Aug 2019



  • Ordinary Differential Equations
  • Particle Swarm Optimization
  • Latin Hypercube Sampling
  • Structure and parameters optimization

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Applied Mathematics

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