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
Dans cette étude, un algorithme génétique hybride/réseau neuronal avec contrôleur à logique floue (NN-flcGA) est proposé pour trouver l'optimum global des problèmes d'attribution de fiabilité/d'allocation redondante qui doivent être déterminés simultanément par deux types différents de variables de décision. Plusieurs chercheurs ont obtenu des résultats acceptables et satisfaisants en utilisant des algorithmes génétiques pour résoudre des problèmes d'attribution de fiabilité optimale/d'allocation redondante au cours de la dernière décennie. Cependant, pour les problèmes de grande envergure, les algorithmes génétiques doivent énumérer de nombreuses solutions réalisables en raison du vaste espace de recherche continu. Récemment, une GA hybridée combinée à une technique de réseau neuronal (NN-hGA) a été proposée pour surmonter ce type de difficulté. Malheureusement, cela nécessite un coût de calcul élevé bien que NN-hGA conduise à un optimal global plus robuste et plus stable quelles que soient les différentes conditions initiales des problèmes. L'efficacité et l'efficience du NN-flcGA sont démontrées en comparant ses résultats avec ceux d'autres méthodes traditionnelles dans des expériences numériques. Les caractéristiques essentielles de NN-flcGA, à savoir 1) sa combinaison avec une technique de réseau neuronal (NN) pour concevoir des valeurs initiales pour le GA, 2) son application du concept de contrôleur à logique floue lors du réglage dynamique des paramètres de la stratégie GA, et 3 ) son incorporation de la méthode de recherche simplexe révisée, permet non seulement d'améliorer la qualité des solutions mais également de réduire le coût de calcul.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
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
Copier
@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},}
Copier
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 -