Ant Colony Optimization and Swarm Intelligence: 4th by Lucas Lessing, Irina Dumitrescu, Thomas Stützle (auth.), PDF

By Lucas Lessing, Irina Dumitrescu, Thomas Stützle (auth.), Marco Dorigo, Mauro Birattari, Christian Blum, Luca Maria Gambardella, Francesco Mondada, Thomas Stützle (eds.)

ISBN-10: 3540226729

ISBN-13: 9783540226727

ISBN-10: 3540286462

ISBN-13: 9783540286462

This publication constitutes the refereed lawsuits of the 4th overseas Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2004, held in Brussels, Belgium in September 2004.

The 22 revised complete papers, 19 revised brief papers, and nine poster abstracts provided have been rigorously reviewed and chosen from seventy nine papers submitted. The papers are dedicated to theoretical and foundational points of ant algorithms, ant colony optimization and swarm intelligence and care for a extensive number of optimization functions in networking and operations research.

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Read Online or Download Ant Colony Optimization and Swarm Intelligence: 4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004. Proceedings PDF

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Additional info for Ant Colony Optimization and Swarm Intelligence: 4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004. Proceedings

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Shanbhag, N. , Hajj, I. : Decorrelating (DECOR) Transformations for Low-Power Digital Filters. IEEE Trans. On Circuits and Systems - II 46 (1999), No. 6, 776–788 10. : Algorithm for Low Power and High Speed FIR Filter Realization Using Differential Coefficients. IEEE Trans. on Circuits and Systems - II 44 (1997) 488–497 11. : Ant-Based Load Balancing in Telecommunications Networks. Adaptive Behavior 5 (1996) 169–207 12. : Ant Systems. be Abstract. This paper presents how the Ant Colony Optimization (ACO) metaheuristic can be extended to continuous search domains and applied to both continuous and mixed discrete-continuous optimization problems.

The new global best solution sG best is compared to the known optimum so . 2 rel = abs rel · so + abs ⇒ STOP (13) = . Results The preliminary results presented here are based on 100 independent runs of the ACO algorithm on each of the test functions. The parameters used for tackling each test function are presented in Tab. 1. In order to have comparable results, the accuracy was chosen based on the results of the other algorithms published in the literature. The other parameters were chosen empirically based on some limited experimentation – about 10 configurations of parameters were tried for 34 Krzysztof Socha Table 1.

Hence, the methods proposed often drew inspiration from ACO, but did not follow exactly the same methodology. Up to now, only a few ant approaches for continuous optimization have been proposed in the literature. The first method – called Continuous ACO (CACO) – was proposed by Bilchev and Parmee [2] in 1995, and also later used by some others [17, 12]. Other methods include the API algorithm by Monmarch´e [13], and Continuous Interacting Ant Colony (CIAC), proposed by Dr´eo and Siarry [9, 8]. M.

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Ant Colony Optimization and Swarm Intelligence: 4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004. Proceedings by Lucas Lessing, Irina Dumitrescu, Thomas Stützle (auth.), Marco Dorigo, Mauro Birattari, Christian Blum, Luca Maria Gambardella, Francesco Mondada, Thomas Stützle (eds.)


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