A modified NM-PSO method for parameter estimation problems of models
type: Item
instance of: scholarly article
title: A modified NM-PSO method for parameter estimation problems of models
zbMATH Open document ID: 1264.49033
DOI: 10.1155/2012/530139
author: An Liu
author: Erwie Zahara
author: Ming-Ta Yang
published in: Journal of Applied Mathematics
publication date: 2013-06-03T00:00:00Z
review text: Summary: Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The Particle Swarm Optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a Modified hybrid Nelder-Mead simplex search and PSO (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional PSO method, and the conventional NM-PSO method.
Mathematics Subject Classification ID: 49M30
Mathematics Subject Classification ID: 49J15
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6169934
zbMATH Keywords: ordinary differential equations
zbMATH Keywords: particle swarm optimization
zbMATH Keywords: modified hybrid Nelder-Mead simplex search
zbMATH Keywords: genetic algorithm
MaRDI profile type: Publication
Wikidata QID: Q58906083
full work available at URL: https://doi.org/10.1155/2012/530139
OpenAlex ID: W2067193771
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description: scientific article; zbMATH DE number 6169934