  A swarm algorithm framework (SWAF), realized by agent-based modeling, is
presented to solve numerical optimization problems. Each agent is a bare bones
cognitive architecture, which learns knowledge by appropriately deploying a set
of simple rules in fast and frugal heuristics. Two essential categories of
rules, the generate-and-test and the problem-formulation rules, are
implemented, and both of the macro rules by simple combination and subsymbolic
deploying of multiple rules among them are also studied. Experimental results
on benchmark problems are presented, and performance comparison between SWAF
and other existing algorithms indicates that it is efficiently.
