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In this book, a methodology for parameter adaptation in
meta-heuristic op-timization methods is proposed. This methodology
is based on using met-rics about the population of the
meta-heuristic methods, to decide through a fuzzy inference system
the best parameter values that were carefully se-lected to be
adjusted. With this modification of parameters we want to find a
better model of the behavior of the optimization method, because
with the modification of parameters, these will affect directly the
way in which the global or local search are performed.Three
different optimization methods were used to verify the improve-ment
of the proposed methodology. In this case the optimization methods
are: PSO (Particle Swarm Optimization), ACO (Ant Colony
Optimization) and GSA (Gravitational Search Algorithm), where some
parameters are se-lected to be dynamically adjusted, and these
parameters have the most im-pact in the behavior of each
optimization method.Simulation results show that the proposed
methodology helps to each optimization method in obtaining better
results than the results obtained by the original method without
parameter adjustment.
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