  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.
