Reliability-Based Optimization Using Evolutionary Algorithms
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Deb_Reliability-Based-Optimization.pdf
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1.8 MB
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Author(s)
Deb, Kalyanmoy
Gupta, Shubham
Daum, David
Branke, Jurgen
Mall, Abhishek Kumar
Padmanabhan, Dhanesh
Date Issued
September 2009
Journal
IEEE Transactions on Evolutionary Computation
Publisher
Institute of Electrical and Electronics Engineers
Citation
Deb, K. et al. “Reliability-Based Optimization Using Evolutionary Algorithms.” Evolutionary Computation, IEEE Transactions on 13.5 (2009): 1054-1074. © 2009 Institute of Electrical and Electronics Engineers
Version
Final published version
Abstract
Uncertainties in design variables and problem parameters are often inevitable and must be considered in an optimization task if reliable optimal solutions are sought. Besides a number of sampling techniques, there exist several mathematical approximations of a solution's reliability. These techniques are coupled in various ways with optimization in the classical reliability-based optimization field. This paper demonstrates how classical reliability-based concepts can be borrowed and modified and, with integrated single and multiobjective evolutionary algorithms, used to enhance their scope in handling uncertainties involved among decision variables and problem parameters. Three different optimization tasks are discussed in which classical reliability-based optimization procedures usually have difficulties, namely (1) reliability-based optimization problems having multiple local optima, (2) finding and revealing reliable solutions for different reliability indices simultaneously by means of a bi-criterion optimization approach, and (3) multiobjective optimization with uncertainty and specified system or component reliability values. Each of these optimization tasks is illustrated by solving a number of test problems and a well-studied automobile design problem. Results are also compared with a classical reliability-based methodology.
Subjects
system reliability
reliable front
reliability-based optimization
pareto-optimal front
most probable point
evolutionary multiobjective optimization
Ditlevsen's bound
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1109/tevc.2009.2014361
