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

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

doi:10.1109/20.877610

A 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

21 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)	1000-1003
Number of pages	4
Journal	IEEE Transactions on Magnetics
Volume	36
Issue number	4 PART 1
DOIs	https://doi.org/10.1109/20.877610
Publication status	Published - 1 Dec 2000

Keywords

Domain elimination method
Global optimization
Self-learning ability
Simulated annealing algorithm

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

More information

10.1109/20.877610

Cite this

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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 - Ni, Guangzheng

AU - Ho, Siu Lau

AU - Zhou, Ping

PY - 2000/12/1

Y1 - 2000/12/1

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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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KW - Global optimization

KW - Self-learning ability

KW - Simulated annealing algorithm

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DO - 10.1109/20.877610

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JF - IEEE Transactions on Magnetics

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

Abstract

Keywords

ASJC Scopus subject areas

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