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Reliability optimization of series-parallel systems using a genetic algorithm
Abstract
A problem-specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems. The problem is to select components and redundancy-levels to optimize some objective function, given system-level constraints on reliability, cost, and/or weight. Previous formulations of the problem have implicit restrictions concerning the type of redundancy allowed, the number of available component choices, and whether mixing of components is allowed. GA is a robust evolutionary optimization search technique with very few restrictions concerning the type or size of the design problem. The solution approach was to solve the dual of a nonlinear optimization problem by using a dynamic penalty function. GA performs very well on two types of problems: (1) redundancy allocation originally proposed by Fyffe, Hines, Lee, and (2) randomly generated problem with more complex k-out-of-n:G configurations.
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Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
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Metadata
- Authors
- Coit, D. W., & Smith, A. E.
- Venue
- IEEE Transactions on Reliability
- Published
- 1996-06
- Volume
- 45
- Issue
- 2
- Pages
- 254-260, 266
- ISSN
- 0018-9529 (ISSN-L)
- OpenAlex
- W2116827549
- Cited by
- 759
- References
- 1
- FWCI
- 47.56
- Type
- journal article
- Language
- en
- Open access
- closed
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