History based learning artificial bee colony algorithm for electromagnetic inverse problems

Xiu Zhang; Xin Zhang; Weinong Fu; S. X. Nu

doi:10.1109/CEFC.2016.7816226

History based learning artificial bee colony algorithm for electromagnetic inverse problems

Xiu Zhang, Xin Zhang, Weinong Fu, S. X. Nu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

Electromagnetic (EM) inverse problems are usually modeled as simulation-based optimization problems with multimodal and non-differentiable properties. These properties justify the invention of more effective optimization algorithms. Artificial bee colony (ABC) has already been applied to tackle EM inverse problems. This paper applies a tree structure to record all search moves of ABC. A history based learning ABC is proposed to assist the finding of promising search directions. Novel variation formula is invented to guide the move of honey bees. The proposed algorithm is applied to tackle a loudspeaker design problem. Simulation results show that the proposed algorithm is more effective and reliable than the compared algorithms.

Original language	English
Title of host publication	IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation
Publisher	IEEE
ISBN (Electronic)	9781509010325
DOIs	https://doi.org/10.1109/CEFC.2016.7816226
Publication status	Published - 12 Jan 2017
Event	17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016 - Hotel Hilton Miami Downtown, Miami, United States Duration: 13 Nov 2016 → 16 Nov 2016

Conference

Conference	17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016
Country/Territory	United States
City	Miami
Period	13/11/16 → 16/11/16

Keywords

Artificial bee colony
Electromagnetic device design
Electromagnetic inverse problem
History based learning

ASJC Scopus subject areas

Computational Mathematics
Instrumentation
Electrical and Electronic Engineering

More information

10.1109/CEFC.2016.7816226

Cite this

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title = "History based learning artificial bee colony algorithm for electromagnetic inverse problems",

abstract = "Electromagnetic (EM) inverse problems are usually modeled as simulation-based optimization problems with multimodal and non-differentiable properties. These properties justify the invention of more effective optimization algorithms. Artificial bee colony (ABC) has already been applied to tackle EM inverse problems. This paper applies a tree structure to record all search moves of ABC. A history based learning ABC is proposed to assist the finding of promising search directions. Novel variation formula is invented to guide the move of honey bees. The proposed algorithm is applied to tackle a loudspeaker design problem. Simulation results show that the proposed algorithm is more effective and reliable than the compared algorithms.",

keywords = "Artificial bee colony, Electromagnetic device design, Electromagnetic inverse problem, History based learning",

author = "Xiu Zhang and Xin Zhang and Weinong Fu and Nu, {S. X.}",

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doi = "10.1109/CEFC.2016.7816226",

language = "English",

booktitle = "IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation",

publisher = "IEEE",

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Zhang, X, Zhang, X, Fu, W & Nu, SX 2017, History based learning artificial bee colony algorithm for electromagnetic inverse problems. in IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation., 7816226, IEEE, 17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016, Miami, United States, 13/11/16. https://doi.org/10.1109/CEFC.2016.7816226

History based learning artificial bee colony algorithm for electromagnetic inverse problems. / Zhang, Xiu; Zhang, Xin; Fu, Weinong et al.

IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation. IEEE, 2017. 7816226.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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T1 - History based learning artificial bee colony algorithm for electromagnetic inverse problems

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AU - Zhang, Xin

AU - Fu, Weinong

AU - Nu, S. X.

PY - 2017/1/12

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N2 - Electromagnetic (EM) inverse problems are usually modeled as simulation-based optimization problems with multimodal and non-differentiable properties. These properties justify the invention of more effective optimization algorithms. Artificial bee colony (ABC) has already been applied to tackle EM inverse problems. This paper applies a tree structure to record all search moves of ABC. A history based learning ABC is proposed to assist the finding of promising search directions. Novel variation formula is invented to guide the move of honey bees. The proposed algorithm is applied to tackle a loudspeaker design problem. Simulation results show that the proposed algorithm is more effective and reliable than the compared algorithms.

AB - Electromagnetic (EM) inverse problems are usually modeled as simulation-based optimization problems with multimodal and non-differentiable properties. These properties justify the invention of more effective optimization algorithms. Artificial bee colony (ABC) has already been applied to tackle EM inverse problems. This paper applies a tree structure to record all search moves of ABC. A history based learning ABC is proposed to assist the finding of promising search directions. Novel variation formula is invented to guide the move of honey bees. The proposed algorithm is applied to tackle a loudspeaker design problem. Simulation results show that the proposed algorithm is more effective and reliable than the compared algorithms.

KW - Artificial bee colony

KW - Electromagnetic device design

KW - Electromagnetic inverse problem

KW - History based learning

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M3 - Conference article published in proceeding or book

BT - IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation

PB - IEEE

T2 - 17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016

Y2 - 13 November 2016 through 16 November 2016

ER -

History based learning artificial bee colony algorithm for electromagnetic inverse problems

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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