Ant Colony Optimization and Swarm Intelligence: 5th by Marco A. Montes de Oca, Thomas Stützle, Mauro Birattari,

By Marco A. Montes de Oca, Thomas Stützle, Mauro Birattari, Marco Dorigo (auth.), Marco Dorigo, Luca Maria Gambardella, Mauro Birattari, Alcherio Martinoli, Riccardo Poli, Thomas Stützle (eds.)

ANTS – The foreign Workshop on Ant Colony Optimization and Swarm Intelligence is now at its ?fth version. The sequence begun in 1998 with the - ganization of ANTS 1998. at the moment the aim used to be to collect in a standard assembly these researchers drawn to ant colony optimization: greater than 50 researchers from around the globe joined for the ?rst time in Brussels, Belgium, to debate ant colony optimization and swarm intelligence comparable examine. A selectionofthebest paperspresentedatthe workshopwaspublished asa detailed factor of the longer term iteration computers magazine (Vol. sixteen, No. eight, 2000). years later, ANTS 2000, geared up back in Brussels, attracted greater than 70 individuals. The forty-one prolonged abstracts provided as talks or posters on the workshopwere gathered in a ebook allotted to individuals, and a variety of the easiest papers used to be released as a unique element of the IEEE Transactions on Evolutionary Computation (Vol. 6, No. four, 2002). After those ?rst winning variations, it was once made up our minds to make of ANTS a seriesofbiannualeventswitho?cialworkshopproceedings.Thethirdandfourth variants have been prepared in September 2002 and September 2004, respectively. complaints have been released by way of Springer in the Lecture Notes in laptop technological know-how (LNCS) sequence. The complaints of ANTS 2002, LNCS quantity 2463, contained 36 contri- tions: 17 complete papers, eleven brief papers, and eight prolonged abstracts,selected out of a complete of fifty two submissions. these of ANTS 2004, LNCS quantity 3172, contained 50 contributions:22 complete papers, 19 shortpapers, and nine prolonged abstracts,selected out of a complete of seventy nine submissions.

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

Sample text

Of course different approaches must be taken to collect information about asymmetric or symmetric link metrics (assuming that all links are bidirectional). For this reason there exist mechanisms such as forward/backward ants (ACO) and data piggybacking (Termite), respectively. For the purposes of this analysis of the presented framework, how the information is collected is irrelevant, only that it is, and that the probe packets follow a routing rule defined by the forwarding equation. There are two commonly used methods for estimating path utility based on samples of the network, both of which are basically low pass filters.

Therefore it is possible to state the perfect equivalence of diversion node probabilities between the Ant approach (25) and the Dial approach (21). With the use of the extension of Blum’s theorem ([14]) proposed by [1], we may state the convergence of the proposed ACO-based MSA algorithm, assuming that existence and uniqueness conditions hold, if Jac [c (τ )] is symmetric and continuous. Indeed, this condition satisfies all hypotheses of Blum’s theorem in the case of the fixed-point problem τ = τ (c (f (τ ))) where τ is a vector whose generic element is the pheromone trail τod,l (or equivalently wod,l ), of dimensions ((nP airs · nLinks ) × 1), and with the assumption that λ (·) = τ (c (f (τ ))) and ϕ (·) : ∇ϕ (·) = c (f (τ ))−c∗ where c∗ = c (f (τ ∗ )).

ANTS 2006, LNCS 4150, pp. 37–48, 2006. c Springer-Verlag Berlin Heidelberg 2006 38 F. A. M. Gambardella all these characteristics, which make up the mode of the network, change over time, and different modes can coexist in different parts of the network. Routing is at the core of the functioning of a MANET, and the challenges mentioned above call for a fully adaptive, multi-modal routing controller. We believe that the multidimensional complexity of the task makes it necessary to include multiple learning, adaptive, and behavioral components in the design of the routing algorithm.

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