摘要：A metaheuristic proposed by us recently, Ant Colony Optimization (ACO) hybridized
with socio-cognitive inspirations, turned out to generate interesting
results when compared to classic ACO. Even though it does not always find
better solutions to the considered problems, it usually finds sub-optimal solutions.
Moreover, instead of a trial-and-error approach to configure the parameters
of the ant species in the population, the actual structure of the population
emerges from a predefined species-to-species ant migration strategies in our
approach. Experimental results of our approach are compared to classic ACO
and selected socio-cognitive versions of this algorithm.
