Download Bio-Inspired Models of Network, Information, and Computing by Gianni A. Di Caro, Guy Theraulaz (eds.) PDF

By Gianni A. Di Caro, Guy Theraulaz (eds.)

This publication constitutes the completely refereed post-conference complaints of the seventh overseas convention on Bio-Inspired versions of community, details and Computing structures (Bionetics 2012), held in Lugano, Switzerland, in December 2012. The 23 revised complete papers provided have been conscientiously reviewed and chosen from forty submissions. They disguise themes reminiscent of networking, robotics and neural networks, molecular scale and bioinformatics, optimization and bio-inspired modeling in a number of fields.

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Additional resources for Bio-Inspired Models of Network, Information, and Computing Systems: 7th International ICST Conference, BIONETICS 2012, Lugano, Switzerland, December 10--11, 2012, Revised Selected Papers

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100, 14086–14090 (2003) 12. : Attractor selection and perturbation for robust networks in fluctuating environments. IEEE Netw. 24, 14–18 (2010) 13. : On reducing time spent in M/G/1 systems. Eur. J. Oper. Res. 9, 286–294 (1982) 14. The network simulator ns-2. pl NASK Research and Academic Computer Network, 02-796 Warsaw, Poland Abstract. This paper introduces the Reactive ASR-FA algorithm which is a novel ant routing algorithm that utilizes statistical models of packet delay to detect changes in the network conditions.

Yet, the NTT network structure is much more complex, so a change of the traffic conditions near one node does not necessarily have to imply a global change. Thus, it is worth to verify the merits in using the separate of Ψ and ψ for each possible destination of a given node. In this set of experiments we test the same scenario as in Sect. 1. The load level is increased after 500 s and returns to the primary value after 1800 s from the beginning of the simulation. We compare the results obtained for Actions 1-3 with the original ASR-FA algorithm for different values of the load level jump.

If at some moment of time, Ψ is unnecessarily decreased, suboptimal paths may be used and the mean delay may grow until Ψ is increased again. We also noticed that — after the adaptation porcess — the mean delay converges to a higher value in case of Action 1 The Reactive ASR-FA 45 and Action 3 (modifying Ψ parameter) than in case of Action 2 and the original ASR-FA algorithm (not changing beta). Additional experiments showed that we can eliminate both problems by setting Ψm = 2, yet this results in a significant increase of the adaptation times and reduces the advantages of our algorithm.

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