NDLI: Genetic algorithms and simulated annealing for robustness analysis

Content Provider	IEEE Xplore Digital Library
Author	Zhu, X. Huang, Y. Doyle, J.
Copyright Year	1997
Description	Author affiliation: California Inst. of Technol., Pasadena, CA, USA (Zhu, X.)
Abstract	Genetic algorithms (GAs) and simulated annealing (SA) have been promoted as useful, general tools for nonlinear optimization. This paper explores their use in robustness analysis with real parameter variations, a known NP hard problem which would appear to be ideally suited to demonstrate the power of GAs and SA. Numerical experiment results show convincingly that they turn out to be poorer than existing branch and bound (B&B) approaches. While this may appear to shed doubt on some of the hype surrounding these stochastic optimization techniques, we find that they do have attractive features, which are also demonstrated in this study. For example, both GAs and SA are almost trivial to understand and program, so they require essentially no expertise, in sharp contrast to the B&B methods. They may be suitable for problems where programming effort is much more important than running time or the quality of the answer. Robustness analysis for engineering problems is not the best candidate in this respect, but it does provide an interesting test case for the evaluation of GAs and SA. A simple hill climbing algorithm is also studied for comparison.
Starting Page	3756
Ending Page	3760
File Size	665455
Page Count	5
File Format	PDF
ISBN	0780338324
ISSN	07431619
DOI	10.1109/ACC.1997.609547
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-06-06
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Genetic algorithms Analytical models Simulated annealing Robustness Algorithm design and analysis NP-hard problem Performance analysis Polynomials Eigenvalues and eigenfunctions Stochastic processes
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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