Calibration of a water and energy balance model: Recursive parameter estimation versus particle swarm optimization

2009 ◽  
Vol 45 (10) ◽  
Author(s):  
Karolien Scheerlinck ◽  
Valentijn R. N. Pauwels ◽  
Hilde Vernieuwe ◽  
Bernard De Baets
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Energy Balance ◽  
Particle Swarm ◽  
Energy Balance Model ◽  
Balance Model ◽  
Swarm Optimization ◽  
Recursive Parameter Estimation ◽  
Versus Particle

GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM

2007 ◽  
Vol 10 (2) ◽  
pp. 172-177
Author(s):  
Azhar W. Hammad ◽  
◽  
Dr. Ban N. Thannoon ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Versus Particle

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

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 particle swarm optimization (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 particle swarm optimization (PSO) method, and the conventional NM-PSO method.

scholarly journals Stiffness Parameter Estimation for Forearm Skeleton Model by Using Particle Swarm Optimization

2016 ◽  
Vol 52 (1) ◽  
pp. 30-39
Author(s):  
Kosei NOJIRI ◽  
Takuya KIYOKAWA ◽  
Hirohumi OHTSUKA ◽  
Yoji OKAYAMA
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Stiffness Parameter ◽  
Swarm Optimization ◽  
Skeleton Model
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