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

Author

Bin Xia ; Baatar, Nyambayar ; Ziyan Ren ; Chang-Seop Koh

Author_Institution

Coll. of Electr. & Comput. Eng., Chungbuk Nat. Univ., Cheongju, South Korea

Volume

51

Issue

3

fYear

2015

fDate

Mar-15

Firstpage

1

Lastpage

4

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.

Keywords

evolutionary computation; finite element analysis; particle swarm optimisation; statistical analysis; B-PSO; DTK; binary particle swarm optimization; dynamic Taylor Kriging; finite-element analysis; fitting error; multiobjective differential evolution algorithm; multiobjective optimization algorithm; objective function values; Accuracy; Algorithm design and analysis; Heuristic algorithms; Linear programming; Optimization; Prediction algorithms; Sociology; Binary particle swarm optimization (B-PSO); dynamic Taylor Kriging (DTK); multi-objective differential evolution (MODE);

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/TMAG.2014.2362938

Filename

7093519