Multi-Objective Optimization of Dynamic Systems combining Genetic Algorithms and Modelica: Application to Adsorption Air-Conditioning Systems

Abstract

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.

A process simulator interface for multiobjective optimization of chemical processes

“…On the one hand, the NSGA-II method is employed via Matlab's function gamultiobj. This is a variant of NSGA-II created to run on Matlab's environment (Bau et al, 2015). On the other hand, algorithms for NBI and NNC are scripted in the CasADi environment.…”