ANN: Societal Implicit Memory and his Speed on Tracking in difficult Environments
Vitorino RAMOS
on Tracking Extrema over Dynamic Environments using Self-Regulatory
Swarms, accepted in Journal of Systems Architecture, Special issue on
Nature Inspired Applied Systems, Elsevier, 2006.
PDF: http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-NIAS06.pdf
ABSTRACT: In order to overcome difficult dynamic optimization and
environment extrema tracking problems, 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
the 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 (DOC) problems.
Measures were made for different dynamic settings and parameters such
as, environmental upgrade frequencies, landscape changing speed
severity, type of dynamic (linear or circular), and to dramatic changes
on the algorithmic search purpose over each test environment (e.g.
shifting the extrema). Finally, comparisons were made with traditional
Genetic Algorithms (GA) as well as with more recently proposed
Co-Evolutionary approaches. SRS, 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 not only to achieve quick adaptive responses, as to
maintaining a number of different solutions, while adapting to new
unforeseen extrema; 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 over dramatic
changing periods, and economizing those foraging resources over periods
of stabilization. Finally, results prove that the present SRS
collective swarm of bio-inspired agents is able to track about 65% of
moving peaks traveling up to ten times faster than the velocity of a
single ant composing that precise swarm tracking system. This emerged
behavior is probably one of the most interesting ones achieved by the
present work.
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