Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66316
Title: History based learning artificial bee colony algorithm for electromagnetic inverse problems
Authors: Zhang, X
Zhang, X
Fu, WN
Nu, SX
Keywords: Artificial bee colony
Electromagnetic device design
Electromagnetic inverse problem
History based learning
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation, 2016, 7816226 How to cite?
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.
Description: 17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016, Miami, US, 13-16 November 2016
URI: http://hdl.handle.net/10397/66316
ISBN: 9781509010325
DOI: 10.1109/CEFC.2016.7816226
Appears in Collections:Conference Paper

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