A Novel Surrogate-Assisted Multi-Objective Optimization Algorithm for an Electromagnetic Machine Design

Title
A Novel Surrogate-Assisted Multi-Objective Optimization Algorithm for an Electromagnetic Machine Design
Author(s)
임동국[임동국]우동균여한결[여한결]정상용[정상용]노종석[노종석]정현교[정현교]
Keywords
PARTICLE SWARM OPTIMIZATION
Issue Date
201503
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON MAGNETICS, v.51, no.3
Abstract
To design electric machines, the motor performance, cost, and manufacturing have to be considered. Hence, researchers have called this the multi-objective optimization (MOO) problem in which the goal is to minimize or maximize several objective functions at the same time. In order to solve the MOO problem, various algorithms, such as nondominated sorting genetic algorithm II and multi-objective particle swarm optimization, have been widely used. When these algorithms are applied to the electric machine design, much time consumption is inevitable due to many times of function evaluations using a finite-element method. To solve this problem, a novel surrogate-assisted MOO algorithm is proposed. Its validity is confirmed by comparing the optimization results of test functions with conventional optimization methods. To verify the feasibility of its application to a practical electric machine, an interior permanent magnet synchronous motor is designed.
URI
http://hdl.handle.net/YU.REPOSITORY/33203http://dx.doi.org/10.1109/TMAG.2014.2359452
ISSN
0018-9464
Appears in Collections:
공과대학 > 전기공학과 > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE