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Constrained design optimization of active magnetic bearings through an artificial immune system

Yu-Cheng Chou (Institute of Undersea Technology, National Sun Yat-sen University, Kaohsiung, Taiwan)
Yi-Hua Fan (Department of Mechanical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan)
Madoka Nakajima (Institute of Undersea Technology, National Sun Yat-sen University, Kaohsiung, Taiwan)
Yi-Lin Liao (Department of Mechanical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan)

Engineering Computations

ISSN: 0264-4401

Article publication date: 7 November 2016

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Abstract

Purpose

The purpose of this paper is to present the use of artificial immune systems (AISs) to solve constrained design optimization problems for active magnetic bearings (AMBs).

Design/methodology/approach

This research applies the AIS approach, more specifically, a representative clonal selection-based AIS called CLONALG, to the single-objective structural design optimization of AMBs. In addition, when compared with a genetic algorithm (GA) developed in the previous work, the CLONALG fails to produce best solutions when a nearly zero feasible ratio occurs in an AMB design problem. Therefore, an AIS called ARISCO (AIS for constrained optimization) is proposed to address the above issue.

Findings

A total of six AMB design cases are solved by the GA, CLONALG, and ARISCO. Based on the simulation results, in terms of solution quality, the ARISCO is shown to have better overall performance than the CLONALG and GA. In particular, when solving a problem with a nearly zero feasible ratio, the ARISCO and GA perform equally and both outperform the CLONALG.

Originality/value

In summary, the contributions of this paper include: this research applies the AIS approach, more precisely, the CLONALG, to the single-objective structural design optimization of AMBs; the ARISCO overall produces better AMB designs than the CLONALG and a GA developed in the previous work; in situations where a nearly zero feasible ratio occurs, the ARISCO and GA perform equally, and they both outperform the CLONALG.

Keywords

Acknowledgements

This research is supported by the Ministry of Science and Technology of Taiwan, under grants MOST 104-2221-E-110-062, MOST 103-2221-E-110-087 and MOST 105-3113-M-110-001.

Citation

Chou, Y.-C., Fan, Y.-H., Nakajima, M. and Liao, Y.-L. (2016), "Constrained design optimization of active magnetic bearings through an artificial immune system", Engineering Computations, Vol. 33 No. 8, pp. 2395-2420. https://doi.org/10.1108/EC-05-2015-0137

Publisher

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Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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