Self-learning simulated annealing algorithm for global optimizations of electromagnetic devices

Shiyou Yang; Jose Marcio Machado; Guangzheng Ni; Siu Lau Ho; Ping Zhou

doi:10.1109/20.877611

Self-learning simulated annealing algorithm for global optimizations of electromagnetic devices

Shiyou Yang, Jose Marcio Machado, Guangzheng Ni, Siu Lau Ho, Ping Zhou

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

13 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1004-1008
Number of pages	5
Journal	IEEE Transactions on Magnetics
Volume	36
Issue number	4 I
DOIs	https://doi.org/10.1109/20.877611
Publication status	Published - 1 Jul 2000

ASJC Scopus subject areas

Electrical and Electronic Engineering
Physics and Astronomy (miscellaneous)

More information

10.1109/20.877611

http://hdl.handle.net/10397/890

Cite this

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title = "Self-learning simulated annealing algorithm for global optimizations of electromagnetic devices",

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.",

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AU - Yang, Shiyou

AU - Machado, Jose Marcio

AU - Ni, Guangzheng

AU - Ho, Siu Lau

AU - Zhou, Ping

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N2 - 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.

AB - 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.

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DO - 10.1109/20.877611

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JO - IEEE Transactions on Magnetics

JF - IEEE Transactions on Magnetics

IS - 4 I

ER -

Self-learning simulated annealing algorithm for global optimizations of electromagnetic devices

Abstract

ASJC Scopus subject areas

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