A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices

Artículos de revistas

Date

2000-07-01

Registration in:

IEEE Transactions on Magnetics. New York: IEEE-Inst Electrical Electronics Engineers Inc., v. 36, n. 4, p. 1004-1008, 2000.

0018-9464

http://hdl.handle.net/11449/130643

10.1109/20.877611

WOS:000090067900084

2-s2.0-0034217702

Author

Hong Kong Polytechnic University

Universidade Estadual Paulista (Unesp)

Zhejiang University

Ansoft Corporation

Institutions

Universidade Estadual Paulista (Brasil)

Abstract

A self-learning simulated annealing algorithm is developed by combining the characteristics of simulated annealing and domain elimination methods. The algorithm is validated by using a standard mathematical function and by optimizing the end region of a practical power transformer. The numerical results show that the CPU time required by the proposed method is about one third of that using conventional simulated annealing algorithm.

Subjects

Domain elimination method

Electromagnetic devices

Power transformer

Self-learning ability

Simulated annealing algorithms

Algorithms

Annealing

Optimization

Electromagnetic fields

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