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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Vydané v:IEEE transactions on magnetics Ročník 51; číslo 3; s. 1 - 4
Hlavní autori: Bin Xia, Baatar, Nyambayar, Ziyan Ren, Chang-Seop Koh
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.03.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Abstract 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.
AbstractList 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.
Author Bin Xia
Ziyan Ren
Baatar, Nyambayar
Chang-Seop Koh
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  surname: Ziyan Ren
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  organization: Coll. of Electr. & Comput. Eng., Chungbuk Nat. Univ., Cheongju, South Korea
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  surname: Chang-Seop Koh
  fullname: Chang-Seop Koh
  email: kohcs@chungbuk.ac.kr
  organization: Coll. of Electr. & Comput. Eng., Chungbuk Nat. Univ., Cheongju, South Korea
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Cites_doi 10.1049/iet-smt:20060035
10.1109/TMAG.2013.2282395
10.1109/TMAG.2007.916091
10.5370/JEET.2014.9.1.184
10.1016/j.asoc.2011.01.017
10.1016/j.pnsc.2008.03.018
10.2514/1.J051017
10.5370/JEET.2008.3.2.207
10.1109/TMAG.2011.2173559
10.1109/TMAG.2011.2174348
10.1109/TMAG.2013.2240285
10.1109/TGRS.2013.2284489
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Keywords dynamic Taylor Kriging (DTK)
Binary particle swarm optimization (B-PSO)
multi-objective differential evolution (MODE)
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SubjectTerms Accuracy
Algorithm design and analysis
Algorithms
Dynamics
Evolution
Fittings
Heuristic algorithms
Kriging
Linear programming
Magnetism
Mathematical analysis
Optimization
Prediction algorithms
Sampling
Sociology
Strategy
Title A Numerically Efficient Multi-Objective Optimization Algorithm: Combination of Dynamic Taylor Kriging and Differential Evolution
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