[embody] On Self-Regulatory Swarms, Societal Memory, Speed and Dynamics

[Vitorino RAMOS]

Societal Implicit Memory and his Speed on Tracking Extrema in Dynamic 
Environments using Self-Regulatory Swarms,
final draft submitted to Journal of Systems Architecture, Special issue on 
Nature Inspired Applied Systems, Elsevier, 2006.
Authors: Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa.

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 through 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), Bacterial foraging algorithms 
(BFOA), as well as with more recent 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.

~ v. ramos [http://alfa.ist.utl.pt/~cvrm/staff/vramos/]