ANN: Self-Regulated Swarms
Vitorino RAMOS
Memory, Speed and Dynamics, in Artificial Life X - Proc. of the Tenth
Int. Conf. on the Simulation and Synthesis of Living Systems, MIT
Press, ISBN 0-262-68162-5, pp. 393-399, Bloomington, Indiana, USA, June
3-7, 2006.
PDF: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-AlifeX06.pdf
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 foraging behavior.
Keeping in mind the above characteristics we propose a Self-Regulated
Swarm (SRS) algorithm which hybridizes the advantageous characteristics
of Swarm Intelligence as the emergence of a societal environmental
memory or cognitive map via collective pheromone laying in the
landscape (properly balancing the exploration/exploitation nature of
our dynamic search strategy), with a simple Evolutionary mechanism that
trough a direct reproduction procedure linked to local environmental
features is able to self-regulate the above exploratory swarm
population, speeding it up globally. In order to test his adaptive
response and robustness, we have recurred to different dynamic
multimodal complex functions as well as to Dynamic Optimization Control
problems, measuring reaction speeds and performance. Final comparisons
were made with standard Genetic Algorithms (GAs), Bacterial Foraging
strategies (BFOA), as well as with recent Co-Evolutionary approaches.
SRS's were able to demonstrate quick adaptive responses, while
outperforming the results obtained by the other approaches.
Additionally, some successful behaviors were found: SRS was able to
maintain a number of different solutions, while adapting to unforeseen
situations even when over the same cooperative foraging period, the
community is requested to deal with two different and contradictory
purposes; the possibility to spontaneously create and maintain
different subpopulations on different peaks, emerging different
exploratory corridors with intelligent path planning capabilities; the
ability to request for new agents (division of labor) over dramatic
changing periods, and economizing those foraging resources over periods
of intermediate stabilization. Finally, results illustrate that the
present SRS collective swarm of bio-inspired ant-like agents is able to
track about 65% of moving peaks traveling up to ten times faster than
the velocity of a single individual composing that precise swarm
tracking system. This emerged behavior is probably one of the most
interesting ones achieved by the present work.
[ comp.ai is moderated ... your article may take a while to appear. ]