Application of Particle Swarm Optimization algorithm on Generator Model Parameters Estimation

2013 ◽  
Author(s):  
Jun-Zhe Yang ◽  
Ching-Jung Liao ◽  
Chun-Feng Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Generator Model

scholarly journals An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 963
Author(s):  
Mohammed Adam Kunna ◽  
Tuty Asmawaty Abdul Kadir ◽  
Muhammad Akmal Remli ◽  
Noorlin Mohd Ali ◽  
Kohbalan Moorthy ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Kinetic Parameters ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Metabolic Model ◽  
Parameters Estimation ◽  
Swarm Optimization ◽  
Distance Minimization ◽  
Kinetic Parameters Estimation

Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.

scholarly journals Calibrating a large network model describing the transmission dynamics of the human papillomavirus using a particle swarm optimization algorithm in a distributed computing environment

2017 ◽  
Vol 32 (5) ◽  
pp. 721-728 ◽  
Author(s):  
Luis Acedo ◽  
Clara Burgos ◽  
José-Ignacio Hidalgo ◽  
Victor Sánchez-Alonso ◽  
Rafael-Jacinto Villanueva ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Computing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Transmission Dynamics ◽  
Model Parameters ◽  
Large Network ◽  
Computing Environment ◽  
Swarm Optimization

Working in large networks applied to epidemiological-type models has led us to design a simple but effective computed distributed environment to perform a large amount of model simulations in a reasonable time in order to study the behavior of these models and to calibrate them. Finding the model parameters that best fit the available data in the designed distributed computing environment becomes a challenge and it is necessary to implement reliable algorithms for model calibration. In this article, we have adapted the random particle swarm optimization algorithm to our distributed computing environment to be applied to the calibration of a papillomavirus transmission dynamics model on a lifetime sexual partners network. And we have obtained a good fitting saving time and calculations compared with the exhaustive searching strategy we have been using so far.

Study on Dynamic Balance in Main Driving Mechanism of Cold Rolling Mill Based on the Particle Swarm Optimization Algorithm

2011 ◽  
Vol 55-57 ◽  
pp. 633-638 ◽  
Author(s):  
Wen Xian Tang ◽  
Jun Jie Sun ◽  
Bin Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Dynamic Balance ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Inertia Force ◽  
Driving Mechanism ◽  
Swarm Optimization

A method for comprehensive dynamic balance of mechanism based on the particle swarm optimization is presented. This paper adopted nonlinear multi-objective programming method to carry out a study on three dynamic property indexes including inertia force, reaction of kinematic pair and input torque. Optimum solution for the parameters estimation problem based on the particle swarm optimization algorithm is obtained by constructing a fitness function of the mathematical optimization model, which consists of those property indexes. The simulation results indicate that the proposed method could eliminate the reluctant evaluations and interactions remarkably, thus improves the application's performance.

scholarly journals USE OF PARTICLE SWARM OPTIMIZATION ALGORITHM FOR DIGITALIZED SINEWAVE SIGNAL PARAMETERS ESTIMATION

2017 ◽  
pp. 203-209
Author(s):  
Pierfrancesco Raimondo
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Classical Method ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Fitting Algorithm ◽  
Swarm Algorithm

In the paper is proposed a procedure based on the particle swarm optimization algorithm for parameters estimation of sinewave signals as: amplitude, phase, frequency and offset. Differently from the classical method used to solve this problem (the sine-fitting algorithms), the proposed procedure considers the estimation problem as an optimization one. In fact, the particle swarm algorithm tends to global solution instead of a local solution. The proposed procedure preliminarily estimates the raw value of the parameters under investigation by a time analysis of the input signal. Successively, these values are used by the particle swarm algorithm for the final estimation result. The tests of the proposed procedure determine the most effective cost function for the algorithm and confirm that the achievable performances are in according with the sine fitting algorithm. Moreover, the execution time for the proposed procedure is lower than the sine fitting, making it an interesting alternative.

Determining PEMFC Model Parameters with IPSO Algorithm

2012 ◽  
Vol 608-609 ◽  
pp. 955-958 ◽  
Author(s):  
Xiao Dong Wang ◽  
Mei Ying Ye ◽  
You Sheng Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Proton Exchange Membrane ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Simulated Data ◽  
Proton Exchange ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

An improved particle swarm optimization algorithm is proposed for determining proton exchange membrane fuel cell (PEMFC) model parameters according to its V-I characteristics. In the algorithm, the weight update is adaptive with the change of objective function. The test results indicate that satisfying parameter accuracy can be achieved by the algorithm. Also, the V-I characteristics obtained by the improved particle swarm optimization algorithm are in good agreement with the simulated data.

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