  In this paper, a novel and generic multi-objective design paradigm is
proposed which utilizes quantum-behaved PSO(QPSO) for deciding the optimal
configuration of the LQR controller for a given problem considering a set of
competing objectives. There are three main contributions introduced in this
paper as follows. (1) The standard QPSO algorithm is reinforced with an
informed initialization scheme based on the simulated annealing algorithm and
Gaussian neighborhood selection mechanism. (2) It is also augmented with a
local search strategy which integrates the advantages of memetic algorithm into
conventional QPSO. (3) An aggregated dynamic weighting criterion is introduced
that dynamically combines the soft and hard constraints with control objectives
to provide the designer with a set of Pareto optimal solutions and lets her to
decide the target solution based on practical preferences. The proposed method
is compared against a gradient-based method, seven meta-heuristics, and the
trial-and-error method on two control benchmarks using sensitivity analysis and
full factorial parameter selection and the results are validated using
one-tailed T-test. The experimental results suggest that the proposed method
outperforms opponent methods in terms of controller effort, measures associated
with transient response and criteria related to steady-state.
