Abstract:
Over the last three decades, many algorithms have
been introduced for solving optimization problems of various
complexity. Previous work in the optimization field on
practical problems, using Cultural Algorithms, had shown that
cultural learning emerged as the result of meta-level swarming
of knowledge sources. This paper explores the use of
meaningful neighbors in Cultural Algorithms for the
constructed social metaphor. The algorithm uses an enhanced
multi-layer tactical restructuring to dynamically change the
topology of agents in the formed networks, periodically during
an algorithm run as a diversity preserving-measure. The
approach has been applied to solve the set of real world
problems proposed for the IEEE-CEC2011 evolutionary
algorithm competition. Our results suggest that under
appropriate parameter settings, the use of modified graphs of
neighborhoods with a probabilistic disruptive re-structuring of
the topology produces better results on the considered test
functions compared to the best known scores of other
algorithms from the literature.