Article

Stigmergic optimization in dynamic binary landscapes

Authors:

Carlos Fernandes,

Vitorino Ramos,

Agostinho C. RosaAuthors Info & Claims

SAC '07: Proceedings of the 2007 ACM symposium on Applied computing

Pages 747 - 748

https://doi.org/10.1145/1244002.1244169

Published: 11 March 2007 Publication History

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Abstract

Hereafter we introduce a novel algorithm for optimization in dynamic binary landscapes. The Binary Ant Algorithm (BAA) mimics some aspects of real social insects' behavior. Like Ant Colony Optimization (ACO), BAA acts by building pheromone maps over a grid of possible trails that represent solutions to an optimization problem. Main differences rely on the way this search space is represented and provided to the colony in order to explore/exploit it. Then, by a process of pheromone reinforcement and evaporation the artificial insect trails converge to regions near the problem solution or extrema. The negative feedback granted by the evaporation mechanism provides the self-organized system with population diversity and self-adaptive characteristics, allowing BAA to be particularly suitable for hard Dynamic Optimization Problems (DOP), where extrema continuously changes at severe speeds.

References

[1]

Abraham, A., Grosan, C., Ramos, V. (Eds.) Stigmergic Optimization. Studies in Computational Intelligence, Vol. 31, Springer-Verlag, 2006.

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Google Scholar

[2]

Dorigo, M., Blum, C. Ant Colony Optimization Theory: A Survey. Theo. Comp. Science, 344, pp. 243--278, 2005.

Digital Library

Google Scholar

[3]

Fernandes, C., Ramos, V., Rosa, A. C. Varying the Population Size of Artificial Foraging Swarms on Time Varying Landscapes. In Art. Neural Networks: Biological Inspirations, LNCS, Vol. 3696, pp. 311--316, 2005.

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Google Scholar

[4]

Kong, M., Tian P. Introducing a Binary Ant Colony Optimization. In Proc. of the 6^th Int. Workshop on ACO and Swarm Intelligence, LNCS, Vol. 4150, pp. 444--451, 2006.

Digital Library

Google Scholar

[5]

Ramos, V., Fernandes, C., Rosa, A. C. On Self-Regulated Swarms, Societal Memory, Speed and Dynamics, In Proc. ALife-X, MIT Press, pp. 393--399, 2006.

Google Scholar

[6]

Rocha, L., Maguitman, A., Huang, C., Kaur, J., Narayanan, S., An Evolutionary model of Genotype Editing, In Proc. ALife-X, MIT Press, pp. 105--111, 2006.

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Cited By

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Iacopino C(2013)Highly Responsive MPS for Dynamic EO ScenariosSpaceOps 2012 Conference10.2514/6.2012-1275545Online publication date: 27-Mar-2013
https://doi.org/10.2514/6.2012-1275545
Iacopino CPalmer PBrewer APolicella NDonati A(2013)EO constellation MPS based on ant colony optimization algorithms2013 6th International Conference on Recent Advances in Space Technologies (RAST)10.1109/RAST.2013.6581192(159-164)Online publication date: Jun-2013
https://doi.org/10.1109/RAST.2013.6581192
Iacopino CPalmer P(2012)The dynamics of ant colony optimization algorithms applied to binary chainsSwarm Intelligence10.1007/s11721-012-0074-36:4(343-377)Online publication date: 6-Dec-2012
https://doi.org/10.1007/s11721-012-0074-3
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Index Terms

Stigmergic optimization in dynamic binary landscapes
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Information

Published In

SAC '07: Proceedings of the 2007 ACM symposium on Applied computing

March 2007

1688 pages

ISBN:1595934804

DOI:10.1145/1244002

Conference Chairs:
Yookun Cho
Seoul National University, Seoul, Korea
,
Roger L. Wainwright
University of Tulsa, Tulsa, Oklahoma
,
Hisham M. Haddad
Kennesaw State University, Kennesaw, Georgia
,
Sung Y. Shin
South Dakota State University, Brookings, South Dakota
,
Program Chair:
Yong Wan Koo
The University of Suwon, Gyeongggi-do, Korea

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 11 March 2007

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SAC07: The 2007 ACM Symposium on Applied Computing

March 11 - 15, 2007

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

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Iacopino C(2013)Highly Responsive MPS for Dynamic EO ScenariosSpaceOps 2012 Conference10.2514/6.2012-1275545Online publication date: 27-Mar-2013
https://doi.org/10.2514/6.2012-1275545
Iacopino CPalmer PBrewer APolicella NDonati A(2013)EO constellation MPS based on ant colony optimization algorithms2013 6th International Conference on Recent Advances in Space Technologies (RAST)10.1109/RAST.2013.6581192(159-164)Online publication date: Jun-2013
https://doi.org/10.1109/RAST.2013.6581192
Iacopino CPalmer P(2012)The dynamics of ant colony optimization algorithms applied to binary chainsSwarm Intelligence10.1007/s11721-012-0074-36:4(343-377)Online publication date: 6-Dec-2012
https://doi.org/10.1007/s11721-012-0074-3
Korošec PŠilc J(2009)The differential ant-stigmergy algorithm applied to dynamic optimization problemsProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689652(407-414)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689652
Korosec PSilc J(2009)The Differential Ant-Stigmergy Algorithm applied to dynamic optimization problems2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4982975(407-414)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4982975

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