The Modelica language enables the fast and convenient development of physical simulation models. These models are often used for simulation studies. The re-use
of simulation models for optimizations requires modeladaptions, additional tools or libraries. In this paper, we present a framework to connect Modelica models developed
in Dymola to MATLAB’s optimization toolbox. As optimization algorithm, we use a multi-objective genetic algorithm. The optimization procedure is tested for an adsorption air-conditioning design. Compared to a full factorial design, the optimization procedure produces better solutions using less evaluations.
multi-objective; optimization; Pareto-solution; dynamic systems; full factorial design; genetic; lgorithms; MATLAB; gamultiobj; NSGA-II; adsorption air-conditioning systems
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