By Rami N. Khushaba, Ahmed Al-Ani, Akram AlSukker, Adel Al-Jumaily (auth.), Marco Dorigo, Mauro Birattari, Christian Blum, Maurice Clerc, Thomas Stützle, Alan F. T. Winfield (eds.)
This publication constitutes the refereed complaints of the sixth overseas Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2008, held in Brussels, Belgium, in September 2008.
The 17 revised complete papers, 24 revised brief papers, and 10 prolonged abstracts awarded have been conscientiously reviewed and chosen from ninety one submissions. The papers disguise theoretical and foundational facets of computational intelligence and comparable disciplines with unique specialize in swarm intelligence and are dedicated to behavioral versions of social bugs and new algorithmic techniques, empirical and theoretical study in swarm intelligence, functions reminiscent of ant colony optimization or particle swarm optimization, and theoretical and experimental learn in swarm robotics systems.
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Additional info for Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings
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Blum, and G. Franc`es 7. : On minimum-energy broadcasting in all-wireless networks. In: Proc. of IEEE LCN, pp. 14–16. IEEE press, Los Alamitos (2001) 8. : A dynamic multicast tree reconstruction algorithm for minimum-energy multicasting in wireless ad hoc networks. In: Proc. of IEEE IPCCC, pp. 637–642. IEEE press, Los Alamitos (2004) 9. : r-shrink: A heuristic for improving minimum power broadcast trees in wireless networks. In: Proc. of GLOBECOM 2003, pp. 523–527. IEEE press, Los Alamitos (2003) 10.
For this ACO metaheuristic we have elected to include expert knowledge as an additional component of the pheromone update rule. When the accuracy of the classifier is identical, ants that choose SNPs with better expert knowledge will contribute more pheromone to those paths than SNPs with lower expert knowledge scores. 1 Implementation This ACO is implemented in C++. For the purposes of this power analysis solutions consist of pairs of attributes. The solution kept as the result is the pair of attributes with the highest balanced accuracy according to MDR (detailed in section 3).
1. For example, while node 47 has a very high emission power in the solution for omni-directional antennas, it only sends information to two neighboring nodes in the solution for directional antennas. 37% Table 2. 49 Ant Colony Optimization for Energy-Efficient Broadcasting 33 34 H. Hern´ andez, C. Blum, and G. Franc`es Table 3. 08 Table 4. 86 Fig. 1. 03. (a) shows the solution in the case of omni-directional antennas, and (b) shows the solution for directional antennas. 4 Conclusions and Future Work In this work we presented an ant colony optimization algorithm using a sophisticated local search procedure for the minimum energy broadcast problem in static wireless ad-hoc networks.