Title: A stable bio-inspired resource assignment strategy for elastic traffic

Authors: Pejman Goudarzi

Addresses: Multimedia Systems Group, IT Faculty, Iran Telecom Research Center (ITRC), Iran

Abstract: Nature-inspired methods have attracted many attentions today for solving difficult and non-linear engineering problems. Some important examples include gravitational search algorithm (GSA), genetic algorithm (GA), DNA computing, artificial immune systems, fuzzy logic etc. Fair network resource allocation strategies are based on solving a form of constrained optimisation problem. There are plenty of high-speed fair rate allocation methods in the literature, some of them are based on fuzzy controllers for improving the convergence speed and are not necessarily optimal. Hence, in the current research, some recent important heuristics such as GA, GSA and their fuzzy-based counterparts have been used for finding the optimal allocated rates. Stability analysis is presented to guarantee the convergence property of the algorithm. After simulating, the results show that using the mixed fuzzy-GSA approach improves the conventional methods in convergence speed and results in fewer oscillations.

Keywords: genetic algorithms; evolutionary computation; gravitational search algorithm; fuzzy GSA; penalty function; bio-inspired resource assignment; elastic traffic; network resource allocation; constrained optimisation; fair rate allocation; simulation; convergence speed; oscillation; fuzzy control; fuzzy logic controllers.

DOI: 10.1504/IJCVR.2015.067152

International Journal of Computational Vision and Robotics, 2015 Vol.5 No.1, pp.55 - 71

Accepted: 16 Feb 2014
Published online: 24 Jan 2015 *

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