Optimization Design of Computer Network Reliability Based on Genetic Algorithms
With the acceleration of the process of information society, not only the user's computer communication networks is increasing, but also the rapid expansion of computer communication network connecting regional scale and network connections. Network reliability optimization of network design is...
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AIDIC Servizi S.r.l.
2016-08-01
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Series: | Chemical Engineering Transactions |
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doaj-fb34c1e283f24ccf8e6dcbc9b6d45b732021-02-19T21:01:42ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162016-08-015110.3303/CET1651130Optimization Design of Computer Network Reliability Based on Genetic AlgorithmsL.J. LiuWith the acceleration of the process of information society, not only the user's computer communication networks is increasing, but also the rapid expansion of computer communication network connecting regional scale and network connections. Network reliability optimization of network design is a classic problem. Due to the complexity of the network reliability with the number of network nodes increases exponentially, it takes too much time to accurate calculation takes. It appears genetic algorithms, neural network, fuzzy neural network intelligent algorithm for solving this problem provides a new ideas and approaches. Due to the complexity of the network reliability with the number of network nodes increases exponentially, we want accurate calculation takes too much time, not even the result. Meanwhile, in order to make a better search algorithm performance, the paper also introduces the concept of co-evolution according to the schema theorem. The introduction of the test group, using a test group to retain better pattern, while the interaction between test groups and reconciliation group, thereby achieve the common purpose of evolution. Finally, the proposed algorithm simulation comparison, the results show that the algorithm has good convergence and search results.https://www.cetjournal.it/index.php/cet/article/view/3994 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
L.J. Liu |
spellingShingle |
L.J. Liu Optimization Design of Computer Network Reliability Based on Genetic Algorithms Chemical Engineering Transactions |
author_facet |
L.J. Liu |
author_sort |
L.J. Liu |
title |
Optimization Design of Computer Network Reliability Based on Genetic Algorithms |
title_short |
Optimization Design of Computer Network Reliability Based on Genetic Algorithms |
title_full |
Optimization Design of Computer Network Reliability Based on Genetic Algorithms |
title_fullStr |
Optimization Design of Computer Network Reliability Based on Genetic Algorithms |
title_full_unstemmed |
Optimization Design of Computer Network Reliability Based on Genetic Algorithms |
title_sort |
optimization design of computer network reliability based on genetic algorithms |
publisher |
AIDIC Servizi S.r.l. |
series |
Chemical Engineering Transactions |
issn |
2283-9216 |
publishDate |
2016-08-01 |
description |
With the acceleration of the process of information society, not only the user's computer communication networks is increasing, but also the rapid expansion of computer communication network connecting regional scale and network connections. Network reliability optimization of network design is a classic problem. Due to the complexity of the network reliability with the number of network nodes increases exponentially, it takes too much time to accurate calculation takes. It appears genetic algorithms, neural network, fuzzy neural network intelligent algorithm for solving this problem provides a new ideas and approaches. Due to the complexity of the network reliability with the number of network nodes increases exponentially, we want accurate calculation takes too much time, not even the result. Meanwhile, in order to make a better search algorithm performance, the paper also introduces the concept of co-evolution according to the schema theorem. The introduction of the test group, using a test group to retain better pattern, while the interaction between test groups and reconciliation group, thereby achieve the common purpose of evolution. Finally, the proposed algorithm simulation comparison, the results show that the algorithm has good convergence and search results. |
url |
https://www.cetjournal.it/index.php/cet/article/view/3994 |
work_keys_str_mv |
AT ljliu optimizationdesignofcomputernetworkreliabilitybasedongeneticalgorithms |
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