Abstract
Performance of optimization algorithms based on metaheuristics
and/or based on derivative-free methods is highly dependent on
its parameters. hen, in order to reach a quality solution as
fast as possible, the algorithm has to be tuned adequately. A
detailed statistical analysis of the system response quality found
by Ant Colony Optimization (ACO) based algorithm with respect
to discretization of the search space and the number of ants
is presented for tuning 4 nonlinear controller structures. he
resulting sensitivity curves permit to determine appropriate ACO
parameter values to initiate the Nelder-Mead (NM) algorithm. A
statistical study of NM convergence is also presented. Using the
results of ACO and NM convergence studies has permited to
reduce the average ACO-NM computation time by up to 7 times for
an equivalent system response quality as compare to the previous
ACO-NM algorithm.
