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Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization

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Listed:
  • Devin Akman
  • Olcay Akman
  • Elsa Schaefer

Abstract

Researchers using ordinary differential equations to model phenomena face two main challenges among others: implementing the appropriate model and optimizing the parameters of the selected model. The latter often proves difficult or computationally expensive. Here, we implement Particle Swarm Optimization, which draws inspiration from the optimizing behavior of insect swarms in nature, as it is a simple and efficient method for fitting models to data. We demonstrate its efficacy by showing that it outstrips evolutionary computing methods previously used to analyze an epidemic model.

Suggested Citation

  • Devin Akman & Olcay Akman & Elsa Schaefer, 2018. "Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization," Journal of Applied Mathematics, John Wiley & Sons, vol. 2018(1).
    • Handle: RePEc:wly:jnljam:v:2018:y:2018:i:1:n:9160793
    DOI: 10.1155/2018/9160793
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    References listed on IDEAS

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    1. Joshua Hallam & Olcay Akman & Füsun Akman, 2010. "Genetic algorithms with shrinking population size," Computational Statistics, Springer, vol. 25(4), pages 691-705, December.
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    Cited by:

    1. Wang, Huanyi & Zhang, Suxia & Xu, Jinhu & Zhang, Xingyu, 2026. "Hierarchical impact of media-induced awareness on epidemic dynamics: Analysis and application of an SIR-M model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 243(C), pages 307-326.
    2. Martina Cendoya & Ana Navarro-Quiles & Antonio López-Quílez & Antonio Vicent & David Conesa, 2025. "An Individual-Based Spatial Epidemiological Model for the Spread of Plant Diseases," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(3), pages 618-637, September.

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