Optimal design problem of system reliability with interval coefficient using improved genetic algorithms

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作者：
Department of Industrial Engineering, Ashikaga Institute of Technology, Ashikaga 326-8558, Japan [1 ]
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来源：
Comput Ind Eng | / 1卷 / 145-149期
关键词：
Approximation theory - Constraint theory - Functions - Genetic algorithms - Integer programming - Nonlinear programming - Problem solving - Systems analysis;
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摘要：
In this paper, we first formulate a De Novo nonlinear integer programming (NIP-I(DN)) problem of system reliability with interval coefficients. It is used for estimating and designing optimal reliability of an incomplete fault detecting and switching (FDS) system. Because of this we are able to use a linear approximation for monotonically increasing reliability function (i.e., objective function) of the original NIP problem. We then transformed it into Knapsack problem with interval coefficients. Last, the problem is solved directly using improved genetic algorithms (IGA) by keeping the nonlinear constraints. We discuss and compare the efficiency between the proposed method and the former one.
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