Bioinspired Models of Network, Information, and Computing by Yezekael Hayel, Emma Hart, Rachid El-Azouzi, Iacopo Carrera,

By Yezekael Hayel, Emma Hart, Rachid El-Azouzi, Iacopo Carrera, Eitan Altman

This publication constitutes the completely refereed post-conference lawsuits of the 4th foreign convention on Bio-Inspired types of community, info, and Computing structures (Bionetics). the development happened within the urban of Avignon in December 2009. Bionetics major target is to carry bio-inspired paradigms into computing device engineereing and networking, and to reinforce the fruitful interactions among those fields and biology. this system of the convention comprises purposes of assorted paradigms that experience their beginning in biology: inhabitants dynamics, branching methods, ant colony optimization. the nineteen revised complete papers have been conscientiously reviewed and chosen and canopy a vast diversity of significant matters in components concerning bio-inspired applied sciences.

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Extra info for Bioinspired Models of Network, Information, and Computing Systems: 4th International Conference, December 9-11, 2009, Revised Selected Papers (Lecture ... and Telecommunications Engineering)

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C Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering 2010 An Evolutionarily Stable Adaptation Framework for Network Applications 37 the paper believe that, if network applications are designed after key biological mechanisms, they may be able to attain autonomy and adaptability as well. In this paper, each network application is designed as a decentralized group of software agents. This is analogous to a bee colony (an application) consisting of multiple bees (agents).

9) If an ant samples an elite path, the behaviour of the system is unmodified. If an ant samples a path that is not in the elite set, at each node along the path the pheromone trail value on the selected link only is reduced, so the probability that an ant samples the same path again is decreased. When the system has converged, the effect of this extension is marginal since most of the ants follow an elite path. On the other hand, when the pheromone distribution becomes wrongly biased after a change, the probability that an ant follows a path that is not in the elite set is high and gradually decreasing the pheromone trail values along this path does make a difference.

N}. Contrary to the total temperature γn∗ , the elite temperature γtˆ used ˆ ˆ = {L(ˆ in (4) to determine Δτt is computed from L ωk ) | k = 1, . . e. ants that have sampled an elite path. χn is self-adjusting; γn∗ adjusts to the network conditions and so does χn . Ants that do not meet the elite selection criterion (6) are discarded, with the exception of explorer ants that are always returned. Hence, the number of ants that backtrack (backward ants) is t ∈ [tˆ, n]. All the operations performed at the destination are summarized in the flow chart shown in Figure 2.

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