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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste estudo, um algoritmo genético híbrido/rede neural com controlador lógico fuzzy (NN-flcGA) é proposto para encontrar o ótimo global de problemas de atribuição de confiabilidade/alocação redundante que devem ser determinados simultaneamente em dois tipos diferentes de variáveis de decisão. Vários pesquisadores obtiveram resultados aceitáveis e satisfatórios usando algoritmos genéticos para problemas de atribuição de confiabilidade ideal/alocação redundante durante a última década. Para problemas de grande porte, entretanto, os algoritmos genéticos precisam enumerar inúmeras soluções viáveis devido ao amplo espaço de busca contínua. Recentemente, um AG hibridizado combinado com uma técnica de rede neural (NN-hGA) foi proposto para superar esse tipo de dificuldade. Infelizmente, requer um alto custo computacional, embora o NN-hGA leve a um ótimo global mais robusto e estável, independentemente das várias condições iniciais dos problemas. A eficácia e eficiência do NN-flcGA são demonstradas comparando seus resultados com os de outros métodos tradicionais em experimentos numéricos. As características essenciais do NN-flcGA são: 1) sua combinação com uma técnica de rede neural (NN) para criar valores iniciais para o GA, 2) sua aplicação do conceito de um controlador lógico fuzzy ao ajustar dinamicamente os parâmetros do GA de estratégia, e 3 ) a sua incorporação do método de busca simplex revisado permite não só melhorar a qualidade das soluções, mas também reduzir custos computacionais.
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ChangYoon LEE, Mitsuo GEN, Yasuhiro TSUJIMURA, "Reliability Optimization Design Using Hybrid NN-GA with Fuzzy Logic Controller" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 2, pp. 432-446, February 2002, doi: .
Abstract: In this study, a hybrid genetic algorithm/neural network with fuzzy logic controller (NN-flcGA) is proposed to find the global optimum of reliability assignment/redundant allocation problems which should be simultaneously determined two different types of decision variables. Several researchers have obtained acceptable and satisfactory results using genetic algorithms for optimal reliability assignment/redundant allocation problems during the past decade. For large-size problems, however, genetic algorithms have to enumerate numerous feasible solutions due to the broad continuous search space. Recently, a hybridized GA combined with a neural network technique (NN-hGA) has been proposed to overcome this kind of difficulty. Unfortunately, it requires a high computational cost though NN-hGA leads to a robuster and steadier global optimum irrespective of the various initial conditions of the problems. The efficacy and efficiency of the NN-flcGA is demonstrated by comparing its results with those of other traditional methods in numerical experiments. The essential features of NN-flcGA namely, 1) its combination with a neural network (NN) technique to devise initial values for the GA, 2) its application of the concept of a fuzzy logic controller when tuning strategy GA parameters dynamically, and 3) its incorporation of the revised simplex search method, make it possible not only to improve the quality of solutions but also to reduce computational cost.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_2_432/_p
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@ARTICLE{e85-a_2_432,
author={ChangYoon LEE, Mitsuo GEN, Yasuhiro TSUJIMURA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Reliability Optimization Design Using Hybrid NN-GA with Fuzzy Logic Controller},
year={2002},
volume={E85-A},
number={2},
pages={432-446},
abstract={In this study, a hybrid genetic algorithm/neural network with fuzzy logic controller (NN-flcGA) is proposed to find the global optimum of reliability assignment/redundant allocation problems which should be simultaneously determined two different types of decision variables. Several researchers have obtained acceptable and satisfactory results using genetic algorithms for optimal reliability assignment/redundant allocation problems during the past decade. For large-size problems, however, genetic algorithms have to enumerate numerous feasible solutions due to the broad continuous search space. Recently, a hybridized GA combined with a neural network technique (NN-hGA) has been proposed to overcome this kind of difficulty. Unfortunately, it requires a high computational cost though NN-hGA leads to a robuster and steadier global optimum irrespective of the various initial conditions of the problems. The efficacy and efficiency of the NN-flcGA is demonstrated by comparing its results with those of other traditional methods in numerical experiments. The essential features of NN-flcGA namely, 1) its combination with a neural network (NN) technique to devise initial values for the GA, 2) its application of the concept of a fuzzy logic controller when tuning strategy GA parameters dynamically, and 3) its incorporation of the revised simplex search method, make it possible not only to improve the quality of solutions but also to reduce computational cost.},
keywords={},
doi={},
ISSN={},
month={February},}
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TY - JOUR
TI - Reliability Optimization Design Using Hybrid NN-GA with Fuzzy Logic Controller
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 432
EP - 446
AU - ChangYoon LEE
AU - Mitsuo GEN
AU - Yasuhiro TSUJIMURA
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2002
AB - In this study, a hybrid genetic algorithm/neural network with fuzzy logic controller (NN-flcGA) is proposed to find the global optimum of reliability assignment/redundant allocation problems which should be simultaneously determined two different types of decision variables. Several researchers have obtained acceptable and satisfactory results using genetic algorithms for optimal reliability assignment/redundant allocation problems during the past decade. For large-size problems, however, genetic algorithms have to enumerate numerous feasible solutions due to the broad continuous search space. Recently, a hybridized GA combined with a neural network technique (NN-hGA) has been proposed to overcome this kind of difficulty. Unfortunately, it requires a high computational cost though NN-hGA leads to a robuster and steadier global optimum irrespective of the various initial conditions of the problems. The efficacy and efficiency of the NN-flcGA is demonstrated by comparing its results with those of other traditional methods in numerical experiments. The essential features of NN-flcGA namely, 1) its combination with a neural network (NN) technique to devise initial values for the GA, 2) its application of the concept of a fuzzy logic controller when tuning strategy GA parameters dynamically, and 3) its incorporation of the revised simplex search method, make it possible not only to improve the quality of solutions but also to reduce computational cost.
ER -