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Computational Chemotaxis in Ants and Bacteria over
Dynamic Environments
70. Ramos, V., Fernandes, C.,
Rosa, A.C., Abraham, A., Computational Chemotaxis in Ants and Bacteria
over Dynamic
Environments, in CEC´07 -
Congress on Evolutionary
Computation, IEEE Press,
USA, ISBN
1-4244-1340-0, pp. 1009-1017, Sep. 2007.
PDF
file: paper
(4.2 MB)
Abstract: Chemotaxis can be defined as an
innate behavioural response by an organism to a directional stimulus,
in which bacteria, and other single-cell or multicellular organisms
direct their movements according to certain chemicals in their
environment. This is important for bacteria to find food (e.g.,
glucose) by swimming towards the highest concentration of food
molecules, or to flee from poisons. Based on self-organized
computational approaches and similar stigmergic concepts we derive a
novel swarm intelligent algorithm. What strikes from these observations
is that both eusocial insects as ant colonies and bacteria have similar
natural mechanisms based on stigmergy in order to emerge coherent and
sophisticated patterns of global collective 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. Later, 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.
Results indicate that the present approach deals well in severe Dynamic
Optimization problems.
Keywords: Distributed Optimization Search in Dynamic Environments, Swarm Intelligence and Perception, Self-Organization,
Stigmergy, Social Cognitive Maps,
Social Foraging.
Related
Works:
64. Societal
Implicit Memory and his
Speed on Tracking Extrema over Dynamic Environments using
Self-Regulatory Swarms.
69. Binary
Ant Algorithm.
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.
65. Stigmergic
Optimization in
Dynamic Binary
Landscapes.
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