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An order reduction problem for linear time invariant models brought to the multi-objective optimization problem is considered. Each criterion is multi-extremum and complex, requires an efficient tool for estimating the parameters of the lower order system and characterizes the model adequacy for the unit-step and Dirac function inputs. A common problem definition is to estimate the lower order model coefficients by minimizing the distance between the output of this model and the initial one. We propose an evolution-based multiobjective stochastic optimization algorithm with a restart operator implemented. The algorithm performance was estimated on two order reduction problems for a single input-single output system and a multiple inputmultiple output one. The effectiveness of the algorithm increased sufficiently after implementing a metaheuristic restart operator. It is shown that the proposed approach is comparable to other approaches, but allows a Pareto-front approximation to be found and not just a single solution.

Multi-objective Order Reduction Problem Solving with Restart Meta-heuristic Implementation