Ant Colony Optimization and Swarm Intelligence: 5th - download pdf or read online

By Chris Sells, Ian Griffiths

ISBN-10: 0596510373

ISBN-13: 9780596510374

This e-book constitutes the refereed lawsuits of the fifth overseas Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2006, held in Brussels, Belgium, in September 2006.

The 27 revised complete papers, 23 revised brief papers, and 12 prolonged abstracts offered have been conscientiously reviewed and chosen from one hundred fifteen submissions. The papers are dedicated to theoretical and foundational points of ant algorithms, evolutionary optimization, ant colony optimization, and swarm intelligence and care for a huge number of optimization functions in networking, operations examine, multiagent platforms, robotic structures, networking, and so on.

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Extra info for Ant Colony Optimization and Swarm Intelligence: 5th International Workshop, ANTS 2006, Brussels, Belgium, September 4-7, 2006, Proceedings

Example text

An analysis of the filter correlation shows that the critical parameter for updating the network with new information is the network sampling rate, or the number of route metric updates sent per network correlation time. A markov chain approach is used to find the steady state routing probabilities given the routing parameters and network costs. The expected path cost from source to destination can then be calculated. The cost is examined to reveal the tradeoffs between sensitivity, threshold, noise, and network sample rate in the context of average behavior and robustness against cost dynamics.

Adv. Manuf. Tech. 22 (2003) 528–533 18. : Implicit enumeration of hyperpaths in a logit model for transit networks. Transport. Sci. 32 (1998) 54–64 An Analysis of the Different Components of the AntHocNet Routing Algorithm Frederick Ducatelle, Gianni A. ch Abstract. Mobile ad hoc networks are a class of highly dynamic networks. In previous work, we developed a new routing algorithm, called AntHocNet, for these challenging network environments. AntHocNet has been designed after the Ant Colony Optimization (ACO) framework, and its general architecture shares strong similarities with the architectures of typical ACO implementations for network routing.

Indeed, as shown by [16], if this phase were performed at each iteration, the set of Dial efficient paths could change at each iteration and therefore (11), which is a necessary condition for verifying convergence of solution algorithms, could not be verified. 4 The Proposed Assignment Algorithm In this paper, we propose an ACO solution algorithm for the fixed-point problem (10) based on the following assumptions: – for each od pair there is an ant colony with its nest (centroid o) and its food source (centroid node d).

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Ant Colony Optimization and Swarm Intelligence: 5th International Workshop, ANTS 2006, Brussels, Belgium, September 4-7, 2006, Proceedings by Chris Sells, Ian Griffiths

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