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A novel steady‐state genetic algorithm approach to the reliability optimization design problem of computer networks
Author(s) -
Mutawa A. M.,
Alazemi H. M. K.,
Rayes Ammar
Publication year - 2008
Publication title -
international journal of network management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.373
H-Index - 28
eISSN - 1099-1190
pISSN - 1055-7148
DOI - 10.1002/nem.687
Subject(s) - crossover , computer science , reliability (semiconductor) , genetic algorithm , population , mutation , network topology , mathematical optimization , convergence (economics) , chromosome , encoding (memory) , operator (biology) , algorithm , computer network , mathematics , artificial intelligence , machine learning , power (physics) , biochemistry , physics , demography , chemistry , repressor , quantum mechanics , sociology , transcription factor , economics , gene , economic growth
This paper introduces the development and implementation of a new methodology for optimizing reliability measures of a computer communication network within specified constraints. A genetic algorithm approach with specialized encoding, crossover, and mutation operators to design a layout topology optimizing source‐terminal computer communication network reliability is presented. In this work, we apply crossover at the gene level in conjunction with the regular chromosome‐level crossover operators that are usually applied on chromosomes or at boundaries of nodes. This approach provides us with a much better population mixture, and hence faster convergence and better reliability. Applying regular crossover and mutation operators on the population may generate infeasible chromosomes representing a network connection. This complicates fitness and cost calculations, since reliability and cost can only be calculated on links that actually exist. In this paper, a special crossover and mutation operator is applied in a way that will always ensure production of a feasible connected network topology. This results in a simplification of fitness calculations and produces a better population mixture that gives higher reliability rates at shorter convergence times. Copyright © 2008 John Wiley & Sons, Ltd.