Multiguiders and Nondominate Ranking Differential Evolution Algorithm for Multiobjective Global Optimization of Electromagnetic Problems

The differential evolution (DE) algorithm was initially developed for single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multiobjective global optimization (MOGO) recently. In general multiobjective d...

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Bibliographic Details
Published in:IEEE transactions on magnetics Vol. 49; no. 5; pp. 2105 - 2108
Main Authors: Baatar, Nyambayar, Pham, Minh-Trien, Koh, Chang-Seop
Format: Journal Article Conference Proceeding
Language:English
Published: New York, NY IEEE 01.05.2013
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Cross-disciplinary physics: materials science; rheology
Differential evolution
Evolution
Evolutionary algorithms
Exact sciences and technology
Feasibility
Indexes
Magnetism
Materials science
Mathematical analysis
multiguiders
multiobjective optimization
nondominated ranking
Optimization
Other topics in materials science
Physics
Sociology
Statistics
Strategy
Studies
Superconducting magnetic energy storage
Testing Electromagnetic Analysis Methods (TEAM) problem 22
Vectors
Vectors (mathematics)
multiobjective optimization
Electromagnetism
Analysis method
Testing Electromagnetic Analysis Methods (TEAM) problem 22
Investigation method
multiguiders
Test method
Differential evolution
nondominated ranking
Magnetic properties
ISSN:0018-9464, 1941-0069
Online Access:Get full text
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Description
Summary:The differential evolution (DE) algorithm was initially developed for single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multiobjective global optimization (MOGO) recently. In general multiobjective differential evolutionary algorithm, only use conventional DE strategies, and, in order to optimize performance constrains problems, the feasibility of the solutions was considered only at selection step. This paper presents a new multiobjective evolutionary algorithm based on differential evolution. In the mutation step, the proposed method which applied multiguiders instead of conventional base vector selection method is used. Therefore, feasibility of multiguiders, involving constraint optimization problems, is also considered. Furthermore, the approach also incorporates nondominated sorting method and secondary population for the nondominated solutions. The propose algorithm is compared with resent approaches of multiobjective optimizers in solving multiobjective version of Testing Electromagnetic Analysis Methods (TEAM) problem 22.
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ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2013.2240285