IEICE TRANSACTIONS on Fundamentals
Reliability Optimization Design Using Hybrid NN-GA with Fuzzy Logic Controller
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E85-A No.2 pp.432-446
- Publication Date
- 2002/02/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Numerical Analysis and Optimization
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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 -
English
