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2018 Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization
Devin Akman, Olcay Akman, Elsa Schaefer
J. Appl. Math. 2018: 1-9 (2018). DOI: 10.1155/2018/9160793

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.

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Devin Akman. Olcay Akman. Elsa Schaefer. "Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization." J. Appl. Math. 2018 1 - 9, 2018. https://doi.org/10.1155/2018/9160793

Information

Received: 25 May 2018; Accepted: 29 July 2018; Published: 2018
First available in Project Euclid: 10 October 2018

zbMATH: 07051370
MathSciNet: MR3854940
Digital Object Identifier: 10.1155/2018/9160793

Rights: Copyright © 2018 Hindawi

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