Parameters’ Identification of Vessel Based on Ant Colony Optimization Algorithm

In this paper, the ant colony optimization (ACO) method is used to identify the parameters of a 3-DOF nonlinear vessel model. Identifying the parameters is abstracted as a nonlinear optimization problem to solve through the ant colony optimization algorithm. The identification procedure is divided i...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2021; pp. 1 - 13
Main Authors: Zhao, Chen, Li, Xiaojian
Format: Journal Article
Language:English
Published: New York Hindawi 26.07.2021
John Wiley & Sons, Inc
Subjects:
Algorithms
Ant colony optimization
Design
Mathematical models
Optimization
Parameter estimation
Parameter identification
Simulation
Support vector machines
Training
Velocity
Yaw
ISSN:1024-123X, 1563-5147
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, the ant colony optimization (ACO) method is used to identify the parameters of a 3-DOF nonlinear vessel model. Identifying the parameters is abstracted as a nonlinear optimization problem to solve through the ant colony optimization algorithm. The identification procedure is divided into two parts. The first part of the identification procedure is to identify the parameters related to surge motion. The second part of the identification procedure is to identify the rest parameters of the vessel’s kinetics model. In the surge model identification procedure, the transient motor speed is used to generate the training data, and in the sway and yaw motion identification procedure, the zigzag maneuvering with different motor speeds is used to generate the training data. All the parameters are identified by the ACO method and the least-square (LS) method based on the training data and then validated on the validation data. The prediction performance of parameters identified by different methods is compared in the simulation to demonstrate the effectiveness of the ACO algorithm.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/6256785