Inferring structure and parameters of dynamic system models simultaneously using swarm intelligence approaches

Muhammad Usman* (Corresponding Author), Wei Pang, George Coghill

*Corresponding author for this work

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Abstract

Inferring dynamic system models from observed time course data is very challenging compared to static system identification tasks. Dynamic system models are complicated to infer due to the underlying large search space and high computational cost for simulation and verification. In this research we aim to infer both the structure and parameters of a dynamic system simultaneously by particle swarm optimization (PSO) improved by efficient stratified sampling approaches. More specifically, we enhance PSO with two modern stratified sampling techniques, i.e., Latin hyper cube sampling (LHS) and Latin hyper cube multi dimensional uniformity (LHSMDU). We propose and evaluate two novel swarm-inspired algorithms, LHS-PSO and LHSMDU-PSO, which can be used particularly to learn the model structure and parameters of complex dynamic systems efficiently. The performance of LHS-PSO and LHSMDU-PSO is further compared with the original PSO and genetic algorithm (GA). We chose real-world cancer biological model called Kinetochores to asses the learning performance of LHSMDU-PSO and LHS-PSO in comparison with GA and the original PSO. The experimental results show that LHSMDU-PSO can find promising models with reasonable parameters and structure, and it outperforms LHS-PSO, PSO, and GA in our experiments.

Original languageEnglish
Pages (from-to)267-282
Number of pages16
JournalMemetic Computing
Volume12
Early online date29 Jul 2020
DOIs
Publication statusPublished - Sep 2020

Keywords

  • Learning structure and parameters
  • Swarm Intelligence
  • Particle swarm optimization
  • Latin hypercube sampling
  • Genetic algorithm
  • Latin hypercube sampling multidimensional uniformity
  • Dynamic systems
  • DIFFERENTIAL-EQUATION MODELS
  • GENERAL-APPROACH
  • INFERENCE

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