IEICE TRANSACTIONS on Fundamentals
Reliability Optimization Design Using a Hybridized Genetic Algorithm with a Neural-Network Technique
Summary :
In this paper, we examine an optimal reliability assignment/redundant allocation problem formulated as a nonlinear mixed integer programming (nMIP) model which should simultaneously determine continuous and discrete decision variables. This problem is more difficult than the redundant allocation problem represented by a nonlinear integer problem (nIP). Recently, several researchers have obtained acceptable and satisfactory results by using genetic algorithms (GAs) to solve optimal reliability assignment/redundant allocation problems. For large-scale problems, however, the GA has to enumerate a vast number of feasible solutions due to the broad continuous search space. To overcome this difficulty, we propose a hybridized GA combined with a neural-network technique (NN-hGA) which is suitable for approximating optimal continuous solutions. Combining a GA with the NN technique makes it easier for the GA to solve an optimal reliability assignment/redundant allocation problem by bounding the broad continuous search space by the NN technique. In addition, the NN-hGA leads to optimal robustness and steadiness and does not affect the various initial conditions of the problems. Numerical experiments and comparisons with previous results demonstrate the efficiency of our proposed method.
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- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.2 pp.627-637
- Publication Date
- 2001/02/01
- Publicized
- Online ISSN
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- Type of Manuscript
- PAPER
- Category
- Numerical Analysis and Optimization
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ChangYoon LEE, Mitsuo GEN, Way KUO, "Reliability Optimization Design Using a Hybridized Genetic Algorithm with a Neural-Network Technique" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 2, pp. 627-637, February 2001, doi: .
Abstract: In this paper, we examine an optimal reliability assignment/redundant allocation problem formulated as a nonlinear mixed integer programming (nMIP) model which should simultaneously determine continuous and discrete decision variables. This problem is more difficult than the redundant allocation problem represented by a nonlinear integer problem (nIP). Recently, several researchers have obtained acceptable and satisfactory results by using genetic algorithms (GAs) to solve optimal reliability assignment/redundant allocation problems. For large-scale problems, however, the GA has to enumerate a vast number of feasible solutions due to the broad continuous search space. To overcome this difficulty, we propose a hybridized GA combined with a neural-network technique (NN-hGA) which is suitable for approximating optimal continuous solutions. Combining a GA with the NN technique makes it easier for the GA to solve an optimal reliability assignment/redundant allocation problem by bounding the broad continuous search space by the NN technique. In addition, the NN-hGA leads to optimal robustness and steadiness and does not affect the various initial conditions of the problems. Numerical experiments and comparisons with previous results demonstrate the efficiency of our proposed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_2_627/_p
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@ARTICLE{e84-a_2_627,
author={ChangYoon LEE, Mitsuo GEN, Way KUO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Reliability Optimization Design Using a Hybridized Genetic Algorithm with a Neural-Network Technique},
year={2001},
volume={E84-A},
number={2},
pages={627-637},
abstract={In this paper, we examine an optimal reliability assignment/redundant allocation problem formulated as a nonlinear mixed integer programming (nMIP) model which should simultaneously determine continuous and discrete decision variables. This problem is more difficult than the redundant allocation problem represented by a nonlinear integer problem (nIP). Recently, several researchers have obtained acceptable and satisfactory results by using genetic algorithms (GAs) to solve optimal reliability assignment/redundant allocation problems. For large-scale problems, however, the GA has to enumerate a vast number of feasible solutions due to the broad continuous search space. To overcome this difficulty, we propose a hybridized GA combined with a neural-network technique (NN-hGA) which is suitable for approximating optimal continuous solutions. Combining a GA with the NN technique makes it easier for the GA to solve an optimal reliability assignment/redundant allocation problem by bounding the broad continuous search space by the NN technique. In addition, the NN-hGA leads to optimal robustness and steadiness and does not affect the various initial conditions of the problems. Numerical experiments and comparisons with previous results demonstrate the efficiency of our proposed method.},
keywords={},
doi={},
ISSN={},
month={February},}
Copy
TY - JOUR
TI - Reliability Optimization Design Using a Hybridized Genetic Algorithm with a Neural-Network Technique
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 627
EP - 637
AU - ChangYoon LEE
AU - Mitsuo GEN
AU - Way KUO
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2001
AB - In this paper, we examine an optimal reliability assignment/redundant allocation problem formulated as a nonlinear mixed integer programming (nMIP) model which should simultaneously determine continuous and discrete decision variables. This problem is more difficult than the redundant allocation problem represented by a nonlinear integer problem (nIP). Recently, several researchers have obtained acceptable and satisfactory results by using genetic algorithms (GAs) to solve optimal reliability assignment/redundant allocation problems. For large-scale problems, however, the GA has to enumerate a vast number of feasible solutions due to the broad continuous search space. To overcome this difficulty, we propose a hybridized GA combined with a neural-network technique (NN-hGA) which is suitable for approximating optimal continuous solutions. Combining a GA with the NN technique makes it easier for the GA to solve an optimal reliability assignment/redundant allocation problem by bounding the broad continuous search space by the NN technique. In addition, the NN-hGA leads to optimal robustness and steadiness and does not affect the various initial conditions of the problems. Numerical experiments and comparisons with previous results demonstrate the efficiency of our proposed method.
ER -
English
