IEEE Transactions on Reliability · 1998 · 322 citations · 5 references
ComputingSystems EngineeringFault-tolerant SystemsInterconnection NetworksOptimizationGenetic AlgorithmsHigh AvailabilityRedundancy ManagementRedundancy TechniquesStructural RedundancyGenetic Algorithm OptimizationParallel RedundancyParallel SystemRedundancy DesignSeries-parallel RedundancySystem Availability AnalysisSystem Redundancy
This paper generalizes a redundancy optimization problem to multi-state systems, where the system and its components have a range of performance levels-from perfect functioning to complete failure. The components are: (1) chosen from a list of products available in the market; and (2) characterized by their nominal performance level, availability and cost. System availability is represented by a multi-state availability function, which extends the binary-state availability. To satisfy the required multi-state system availability, the redundancy for each component can be used. A procedure which determines the minimal-cost series-parallel system structure subject to a multi-state availability constraint is proposed. A fast procedure is developed, based on a universal generating function, to evaluate the multi-state system availability. Two important types of systems are considered and special operators for the universal generating function determination are introduced. A genetic algorithm is used as an optimization technique. Examples are given.
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Genetic algorithms in search, optimization, and machine learning
Choice Reviews Online · 1989 · 49.3K citations
An introduction to genetic algorithms
Scott Austin · AI Expert archive · 1990 · 52 citations