Multiobjective evolutionary optimization of MEMS structures

• Autorzy: Długosz A.
• Języki publikacji: EN

Abstrakty

EN

The paper is devoted to the shape optimization of piezoelectric and electro-thermo-mechanical devices by the use of multiobjective evolutionary algorithm. In this paper, special implementation of multiobjective evolutionary algorithm is applied (MOOPTIM). Several test problems are solved in order to test efficiency of the algorithm. The results are compared with the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The objective function values are calculated for each chromosome in every generation by solving a boundary value problem for the piezoelectricity and electro-thermal-mechanical analysis. In order to solve the boundary value problems, the finite element method is used. Different functionals based on the results derived from coupled field analyses are formulated. The aim of the multiobjective problem is to determine the specific dimensions of the optimized structures. Numerical examples for multiobjective shape optimization are enclosed.

Słowa kluczowe

EN

multiobjective optimization; evolutionary algorithm; piezoelectricity; electro-thermomechanical analysis; finite element method; MEMS

PL

optymalizacja wielokryterialna; algorytm ewolucyjny; piezoelektryka; electrocieplna analiza; metoda elementów skończonych; MEMS

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2010
• Tom: Vol. 17, no. 1
• Strony: 41--50
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Długosz A.

• Department of Strength of Materials and Computational Mechanics, Silesian University of Technology, Gliwice, Poland,

Bibliografia

• [1] G. Beer. Finite element, boundary element and coupled analysis of unbounded problems in elastostastics. Int. J. Numer. Meth. Eng., 19: 567-580, 1983.
• [2] T. Burczyński, W. Kuś. Distributed evolutionary algorithm - tests and applications. In: Proc. AI-METH 2002, Gliwice, 2002.
• [3] K. Deb. Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation, 7(3): 205-230, 1999.
• [4] K. Deb. Evolutionary Multi-Objective Optimization Without Additional Parameters. In: J. Kacprzyk, ed., Studies in Computational Intelligence, 54, pp. 241-257, Springer-Verlag, Berlin-Heidelberg, 2007.
• [5] K. Deb, S. Agrawal, A. Pratap, T. Meyarivan. A Fast Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2): 182-197, 2002.
• [6] Documentation for ANSYS. ANSYS Inc., 2007.
• [7] B. Fu, T. Hemsel, J. Wallaschek. Piezoelectric transducer design via multiobjective optimization. Ultrasonics, 44: e747-e752, 2006.
• [8] L. Gaul, M. Kogl. A boundary element method for transient piezoelectric analysis. Engineering Analysis with Boundary Elements, 24: 591-598, 2000.
• [9] W. Kuś. Grid-enabled evolutionary algorithm application in the mechanical optimization problems. Engineering Applications of Artificial Intelligence, 20(5): 629-636, 2007.
• [10] N. Maluf, K. Williams. An introduction to microelectromechanical systems engineering. Artech House Publishers, 2004.
• [11] Z. Michalewicz. Genetic algorithms + data structures = evolutionary programs. Springer-Verlag, Berlin-New York, 1996.
• [12] H.F. Tiersten. Linear piezoelectric plate vibrations. Plenum Press, New York, 1969.
• [13] Y. Zhang, Q. Huang, R. Li, W. Li. Macro-modeling for polysilicon cascaded bent beam electrothermal microactuators. Sensors and actuators A: Physical, 128(1): 165-175, 2006.
• [14] O.C. Zienkiewicz, R.L. Taylor. The Finite Element Method. Butterworth-Heinemann, Oxford, 2000.
• [15] E. Zitzler, M. Laumanns, L. Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. TIK-Report 103, 2001.