|
On Ants, Bacteria and Dynamic Environments
58. Vitorino Ramos,
Carlos Fernandes and Agostinho C. Rosa, On Ants, Bacteria and Dynamic
Environments, in NCA-05,
Natural Computing and Applications Workshop,
Universitatea de Vest Din Timisoara brochure, pp. 74-81, Timisoara,
Romania, Sep. 25-29, 2005.
PDF
file: paper
(4.2 MB)
Abstract: Wasps, bees, ants and termites
all make effective use of their environment and resources by displaying
collective “swarm” intelligence. Termite colonies - for instance -
build nests with a complexity far beyond the comprehension of the
individual termite, while ant colonies dynamically allocate labor to
various vital tasks such as foraging or defense without any central
decision-making ability. Recent research suggests that microbial life
can be even richer: highly social, intricately networked, and teeming
with interactions, as found in bacteria. What strikes from these
observations is that both ant colonies and bacteria have similar
natural mechanisms based on Stigmergy and Self-Organization in order to
emerge coherent and sophisticated patterns of global behaviour. Keeping
in mind the above characteristics we will present a simple model to
tackle the collective adaptation of a social swarm based on real ant
colony behaviors (SSA algorithm) for tracking extrema in dynamic
environments and highly multimodal complex functions described in the
well-know De Jong test suite. Then, for the purpose of comparison, a
recent model of artificial bacterial foraging (BFOA algorithm) based on
similar stigmergic features is described and analyzed. Final results
indicate that the SSA collective intelligence is able to cope and
quickly adapt to unforeseen situations even when over the same
cooperative foraging period, the community is requested to deal with
two different and contradictory purposes, while outperforming BFOA in
adaptive speed.
Keywords: Swarm Intelligence and Perception, Social Cognitive Maps,
Social Foraging, Self-Organization, Stigmergy, Distributed Search and
Optimization in Dynamic Environments.
Cited
by:
º
Jing
Dang, Anthony Brabazon, Michael O’Neill and David Edelman, "Estimation
of an EGARCH Volatility Option Pricing Model using a Bacteria Foraging
Optimisation Algorithm", in Natural Computing in Computational Finance,
Book Series Studies in Computational Intelligence, ISBN
978-3-540-77476-1, pp. 109-127, Vol. 100/08, Springer Berlin /
Heidelberg, 2008.
º Chunguo
Wu, Na Zhang, Jingqing Jiang, Jinhui Yang, Yanchun Liang, "Improved
Bacterial Foraging Algorithms and Their Applications to Job Shop
Scheduling Problems", in Adaptive and Natural Computing Algorithms,
LNCS, Vol. 4431, pp. 562-569, Springer-Verlag, July 2007.
º Jeff
Jones and Mohammed Saeed, "Image Enhancement – An Emergent Pattern
Formation approach via Decentralised Multi-Agent Systems", in
Multiagent and Grid Systems Journal (Special Issue on Nature inspired
systems for parallel, asynchronous and decentralised environments),
Enda Ridge, Edward Curry, Daniel Kudenko, Dimitar Kazakov (Eds.), Vol.
3, n. 1/07, pp. 105-140, IOS Press, April 2007.
º Kauko
Leiviskä, Iiris Joensuu, "Chemotaxis for Controller Tuning", in
NiSIS 2006 - The 2nd European Symposium on Nature-inspired Smart
Information Systems, Puerto de la Cruz, Tenerife, Spain, 29 Nov. - 1
Dec., 2006.
Related
Works:
70. Computational
Chemotaxis
in Ants and Bacteria
over Dynamic
Environments.
64. Societal
Implicit Memory and his
Speed on Tracking Extrema over Dynamic Environments using
Self-Regulatory Swarms.
61. On
Self-Regulated Swarms, Societal
Memory, Speed and Dynamics.
56. Varying
the
Population Size of
Artificial Foraging Swarms on Time Varying Landscapes.
63. Social
Cognitive Maps, Swarm
Collective Perception and Distributed Search on Dynamic Landscapes.
|