The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 연속형 결정변수와 이산형 결정변수를 동시에 결정해야 하는 비선형 혼합정수계획법(nMIP) 모델로 공식화된 최적 신뢰도 할당/중복 할당 문제를 검토한다. 이 문제는 비선형 정수 문제(nIP)로 표현되는 중복 할당 문제보다 더 어렵습니다. 최근 몇몇 연구자들은 최적 신뢰도 할당/중복 할당 문제를 해결하기 위해 유전자 알고리즘(GA)을 사용하여 수용 가능하고 만족스러운 결과를 얻었습니다. 그러나 대규모 문제의 경우 GA는 광범위한 연속 검색 공간으로 인해 실행 가능한 솔루션을 엄청나게 많이 열거해야 합니다. 이러한 어려움을 극복하기 위해 우리는 최적의 연속 솔루션을 근사하는 데 적합한 신경망 기술(NN-hGA)과 결합된 하이브리드 GA를 제안합니다. GA와 NN 기술을 결합하면 NN 기술로 광범위한 연속 검색 공간을 제한함으로써 GA가 최적의 신뢰도 할당/중복 할당 문제를 더 쉽게 해결할 수 있습니다. 또한 NN-hGA는 최적의 견고성과 안정성을 제공하며 문제의 다양한 초기 조건에 영향을 미치지 않습니다. 수치 실험과 이전 결과와의 비교는 제안된 방법의 효율성을 보여줍니다.
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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
부
@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},}
부
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 -