Parameter Identification of Chaotic Systems Using an Improved Artificial Bee Colony Algorithm

2017 ◽  
Author(s):  
Dongli Zhang ◽  
Yinggan Tang
Keyword(s):  
Parameter Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Chaotic Systems ◽  
Bee Colony

Improving exploration property of velocity-based artificial bee colony algorithm using chaotic systems

2018 ◽  
Vol 465 ◽  
pp. 130-143 ◽  
Author(s):  
Parham Moradi ◽  
Nafiseh Imanian ◽  
Nooruldeen Nasih Qader ◽  
Mahdi Jalili
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Chaotic Systems ◽  
Bee Colony

scholarly journals Flutter Derivatives Identification and Uncertainty Quantification for Bridge Decks Based on the Artificial Bee Colony Algorithm and Bootstrap Technique

2021 ◽  
Vol 11 (23) ◽  
pp. 11376
Author(s):  
Zhouquan Feng ◽  
Yang Lin
Keyword(s):  
Parameter Identification ◽  
Uncertainty Quantification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Bridge Decks ◽  
Weighted Least Squares ◽  
Bee Colony ◽  
Bootstrap Technique ◽  
Sectional Model ◽  
Flutter Derivatives

This paper presents a novel parameter identification and uncertainty quantification method for flutter derivatives estimation of bridge decks. The proposed approach is based on free-decay vibration records of a sectional model in wind tunnel tests, which consists of parameter identification by a heuristic optimization algorithm in the sense of weighted least squares and uncertainty quantification by a bootstrap technique. The novel contributions of the method are on three fronts. Firstly, weighting factors associated with vertical and torsional motion in the objective function are determined more reasonably using an iterative procedure rather than preassigned. Secondly, flutter derivatives are identified using a hybrid heuristic and classical optimization method, which integrates a modified artificial bee colony algorithm with the Powell’s algorithm. Thirdly, a statistical bootstrap technique is used to quantify the uncertainties of flutter derivatives. The advantages of the proposed method with respect to other methods are faster and more accurate achievement of the global optimum, and refined uncertainty quantification in the identified flutter derivatives. The effectiveness and reliability of the proposed method are validated through noisy data of a numerically simulated thin plate and experimental data of a bridge deck sectional model.

Sign in / Sign up

Export Citation Format

Share Document