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Evolutionary Neural Networks algorithm for the Dynamic Frequency Assignment Problem

ISSN:0975-3826
Journal from gdlhub / 2017-08-14 11:52:33
Oleh : Jamal Elhachmi and Zouhair Guennoun, STIKOM Dinamika Bangsa Jambi
Dibuat : 2012-06-26, dengan 1 file

Keyword : Frequency assignment, combinatorial optimization, genetic algorithms, neural networks.
Subjek : Evolutionary Neural Networks algorithm for the Dynamic Frequency Assignment Problem
Url : http://airccse.org/journal/jcsit/0611csit04.pdf
Sumber pengambilan dokumen : Internet

Wireless communication is used in many different situations such as mobile telephony, radio and TV


broadcasting, satellite communication, wireless LANs, and military operations. In each of these situations


a frequency assignment problem arises with application-specific characteristics. Researchers have


developed different modelling ideas for each of the features of the problem, such as the handling of


interference among radio signals, the availability of frequencies, and the optimization criterion.


This paper presents a new approach for solving the problem of frequency allocation based on using


initially a partial solution respecting all constraints according to a greedy algorithm. This partial


solution is then used for the construction of our stimulation in the form of a neural network. In a second


step, the approach will use searching techniques used in conjunction with iterative algorithms for the


optimization of the parameters and topology of the network. The iterative algorithms used are named


hierarchical genetic algorithms (HGA).


Our approach has been tested on standard benchmark problems called Philadelphia problems of


frequency assignment. The results obtained are equivalent to those of current methods. Moreover, our


approach shows more efficiency in terms of flexibility and autonomy

Deskripsi Alternatif :

Wireless communication is used in many different situations such as mobile telephony, radio and TV


broadcasting, satellite communication, wireless LANs, and military operations. In each of these situations


a frequency assignment problem arises with application-specific characteristics. Researchers have


developed different modelling ideas for each of the features of the problem, such as the handling of


interference among radio signals, the availability of frequencies, and the optimization criterion.


This paper presents a new approach for solving the problem of frequency allocation based on using


initially a partial solution respecting all constraints according to a greedy algorithm. This partial


solution is then used for the construction of our stimulation in the form of a neural network. In a second


step, the approach will use searching techniques used in conjunction with iterative algorithms for the


optimization of the parameters and topology of the network. The iterative algorithms used are named


hierarchical genetic algorithms (HGA).


Our approach has been tested on standard benchmark problems called Philadelphia problems of


frequency assignment. The results obtained are equivalent to those of current methods. Moreover, our


approach shows more efficiency in terms of flexibility and autonomy

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