A Numerically Efficient Multi-Objective Optimization Algorithm: Combination of Dynamic Taylor Kriging and Differential Evolution

A dynamic Taylor Kriging (DTK) is newly developed and combined with a multi-objective differential evolution algorithm to get a numerically efficient multi-objective optimization strategy. In the DTK, basis functions are not predefined but optimally selected so that the fitting error with the given...

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Bibliographic Details
Published in:IEEE transactions on magnetics Vol. 51; no. 3; pp. 1 - 4
Main Authors: Bin Xia, Baatar, Nyambayar, Ziyan Ren, Chang-Seop Koh
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithm design and analysis
Algorithms
Dynamics
Evolution
Fittings
Heuristic algorithms
Kriging
Linear programming
Magnetism
Mathematical analysis
Optimization
Prediction algorithms
Sampling
Sociology
Strategy
dynamic Taylor Kriging (DTK)
Binary particle swarm optimization (B-PSO)
multi-objective differential evolution (MODE)
ISSN:0018-9464, 1941-0069
Online Access:Get full text
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Summary:A dynamic Taylor Kriging (DTK) is newly developed and combined with a multi-objective differential evolution algorithm to get a numerically efficient multi-objective optimization strategy. In the DTK, basis functions are not predefined but optimally selected so that the fitting error with the given sampling data may be minimized. In the developed multi-objective optimization algorithm, the DTK provides predicted objective function values as an alternative to direct finite-element analysis. The effectiveness of the proposed DTK and multi-objective optimization strategy are verified through applications to analytic example and TEAM 22.
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ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2014.2362938