324 research outputs found

    An Ant-based Approach for Dynamic RWA in Optical WDM Networks

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    A bio-inspired scheduling scheme for wireless sensor networks

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    Author name used in this publication: Chi K. TseAuthor name used in this publication: Francis C. M. LauRefereed conference paper2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

    Impact of Number of Artificial Ants in ACO on Network Convergence Time: A Survey

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    Due to the dynamic nature of computer networks today, there is need to make the networks self-organized. Selforganization can be achieved by applying intelligent systems in the networks to improve convergence time. Bio-inspired algorithms that imitate real ant foraging behaviour of natural ants have been seen to be more successful when applied to computer networks to make the networks self-organized. In this paper, we studied how Ant Colony Optimization (ACO) has been applied in the networks as a bio-inspired algorithm and its challenges. We identified the number of ants as a drawback to guide this research. We retrieved a number of studies carried out on the influence of ant density on optimum deviation, number of iterations and optimization time. We found that even though some researches pointed out that the numbers of ants had no effect on algorithm performance, many others showed that indeed the number of ants which is a parameter to be set on the algorithm significantly affect its performance. To help bridge the gap on whether or not the number of ants were significant, we gave our recommendations based on the results from various studies in the conclusion section of this pape

    Multiple Feasible Paths in Ant Colony Algorithm for mobile Ad-hoc Networks with Minimum Overhead

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    Mobile ad-hoc networks are infrastructure-less networks consisting of wireless, possibly mobile nodes which are organized in peer-to-peer and autonomous fashion. The highly dynamic topology, limited bandwidth availability and energy constraints make the routing problem a challenging one. Ant colony optimization (ACO) is a population based meta-heuristic for combinatorial optimization problems such as communication network routing problem. In real life, ants drop some kind of chemical substances to mark the path that they used. Then on their way, back they choose the path with the highest pheromones which becomes the shortest path. But Ant net Algorithms may cause the network congestion and stagnation. Here, multiple optimal paths are proposed with negligible overhead in spite of single optimal path in Ant net routing algorithm, so that the problem of stagnation can be rectified. This paper proposes an improved Multiple Feasible Paths in Ant Colony Algorithm for mobile Ad-hoc Networks with Minimum Overhead
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