Hubert Thieriot
Center For Energy and Processes, MINES ParisTech, Palaiseau, France
Maroun Nemura
Center For Energy and Processes, MINES ParisTech, Palaiseau, France
Mohsen Torabzadeh-Tari
PELAB Programming Environment Lab, Dept. Computer Science, Linkoping University Sweden
Peter Fritzson
PELAB Programming Environment Lab, Dept. Computer Science, Linkoping University Sweden
Rajiv Singh
Evonik Energy Services, Pvt. Ltd., India
John John Kocherry
Evonik Energy Services, Pvt. Ltd., India
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http://dx.doi.org/10.3384/ecp11063756Published in: Proceedings of the 8th International Modelica Conference; March 20th-22nd; Technical Univeristy; Dresden; Germany
Linköping Electronic Conference Proceedings 63:84, p. 756-762
Published: 2011-06-30
ISBN: 978-91-7393-096-3
ISSN: 1650-3686 (print), 1650-3740 (online)
One of the main goals when modeling a physical system is to optimize its design or configuration. Currently existing platforms are often dependent on commercial software or are based on in-house and special-purpose development tools. These two alternatives present disadvantages that limit sharing and reusability. The same assessment has partly motivated the origin of the Modelica language itself. In this paper; a new optimization platform called OMOptim is presented. Intrinsically linked with OpenModelica; this platform is mainly aimed at facilitating optimization algorithm development; as well as application use together with models. A first version is already available and three test cases of which one using respectively Dymola and two using OpenModelica are presented. Future developments and design considerations of OMOptim but also of related OpenModelica computation functions are also discussed.
Optimization; model-based; parameter; genetic algorithm; Modelica; modeling; simulation
[1] Ceres project : www.ecleer.com/ecleer_i_15/.
[2] Edop project : http://openmodelica.org/index.php/research/omoptim/edop.
[3] D. Goldberg; Genetic algorithms in search; optimization; and machine learning; Addison-wesley; 1989.
[4] F. Glover; Future paths for integer programming and links to artificial intelligence; Comp. Oper. Res. 13 (1986) 533-549.
[5] S. Kirkpatrick; C. Gelatt; M. Vecchi; Optimization by simulated annealing; Science 220 (1983).
[6] J. Holland; Adaptation in Natural and Artificial Systems; Ann Arbor; MI; 1975.
[7] T. Bäck; H. Schwefel; An overview of evolutionary algorithms for parameter optimization; Evolutionary computation 1 (1993) 1-23.
doi: 10.1162/evco.1993.1.1.1.
[8] Itea2; modelisar : www.itea2.org.
[9] Openmodelica system documentation; available on http://www.openmodelica.org.
[10] A. Liefooghe; M. Basseur; L. Jourdan; E.-G. Talbi; Paradiseo-moeo: A framework for evolutionary multi-objective optimization; 2007.
[11] E. Zitzler; M. Laumanns; L. Thiele; Spea2: Improving the strength pareto evolutionary algorithm; in: EUROGEN; Citeseer; 2001; pp. 95-100.
[12] K. Deb; H. Beyer; Self-adaptation in real-parameter genetic algorithms with simulated binary crossover; in: Proceedings of the Genetic and Evolutionary Computation Conference; Citeseer; 1999; pp. 172-9.
[13] R. Murr; H. Thieriot; A. Zoughaib; D. Clodic; Multi-objective optimization of a multi water-to-water heat pump system using evolutionary algorithm; Submitted to Applied Energy. ().
[14] P. Fritzson; Principles of Object-Oriented Modeling and Simulation with Modelica 2.1; Wiley-IEEE Press; 2004.
[15] A. Hopgood; L. Nolle; A. Battersby; Hybrid genetic algorithms: A review (2006) -.
[16] M. Fesanghary; M. Mahdavi; M. Minary-Jolandan; Y. Alizadeh; Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems; Computer methods in applied mechanics and engineering 197 (2008) 3080-3091.
doi: 10.1016/j.cma.2008.02.006.
[17] S. Katare; A. Bhan; J. Caruthers; W. Delgass; V. Venkatasubramanian; A hybrid genetic algorithm for efficient parameter estimation of large kinetic models; Computers & chemical engineering 28 (2004) 2569-2581.
doi: 10.1016/j.compchemeng.2004.07.002.
[18] H. Lundvall; P. Fritzson; Automatic parallelization using pipelining for equation-based simulation languages; in: In proceedings of the 14th Workshop on Compilers for Parallel Computing (CPC’2009); Zurich; Switzerland.
[19] P. stlund; K. Stavker; P. Fritzson; Parallel simulation of equation-based models on cuda-enabled gpus; Submitted to EuroPar (2010).
[20] J. Åkesson; K.-E. Arzen; M. Gafvert; T. Bergdahl; H. Tummescheit; Modeling and optimization with optimica and jmodelica.org-languages and tools for solving large-scale dynamic optimization problems; Computers & Chemical Engineering 34 (2010) 1737-1749.
doi: 10.1016/j.compchemeng.2009.11.011.
[21] Dassault systemes; www.3ds.com.
[22] Isight product page : http://www.simulia.com/products/isight.html.
[23] Optiy; multidisciplinary analysis and optimization; http://www.optiy.eu.
[24] Modefrontier; http://www.modefrontier.com.
[25] Genopt; http://simulationresearch.lbl.gov/go/.
[26] H. Nilsson; G. Giorgidze; Exploiting structural dynamism in functional hybrid modeling for simulation of ideal diodes; in: Eurosim 2010.
