Applications of evolutionary strategies in optimal design of mechanical systems

• Autorzy: Datoussaid S. , Guerlement G. , Descamps T.
• Języki publikacji: EN

Abstrakty

EN

This paper is concerned with the optimization of structural systems. On one hand it concerns the kinematic or dynamic behaviour of multibody systems. On the other hand it concerns the minimum weight desmg of frames according to static and dynamic constraints. The authors first recall the classical form of an optimization problem, whose purpose is to find the set of design variables which minimizes a given objective function while verifying potentialconstraints. After recalling the principal optimization techniques, the evolutionary strategies are presented in detail. They are inspired from the natural evolution: the genes of individuals mutate from generation to generation and the survivings are those being the best fitted to their environment. The analogy with an optimization problem is quite straightforward: a set of design variables can be considered as the genes of an individual and the value of the objective function for this set of design variables represents the fitness for survival of the corresponding individual. Practically, the mutation is performed by modifying the design variables of μ parents, according to a normal distribution with zero as average and gives rise to offsprings whose best μ individuals form the new parent population. The paper gives some indica tions for the choice of the principal parameters or options and explains how to manage the mutation in order to control the speed of convergence. The performances of the evolutionary strategies are then illustrated by three examples: the structural optimization of a two-storey steel frame, the kinematic optimization of a suspension and the dynamic optimization of the comfort of a railway vehicle. Evolutionary strategies, although slower than hill-climbing methods, arc an interesting alternative. They indeed have several advantages: the optimization engine remains completely independent of the simulation one and can be adapted to any field of engineering, they are very robust and converge to global and not local optimal solutions.

Słowa kluczowe

EN

multibody methods; kinematic; dynamics; structure; optimization; evolutionary strategy

PL

kinematyka; dynamika; konstrukcja

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Civil and Environmental Engineering
• Rocznik: 2006
• Tom: No. 7
• Strony: 35--51
• Opis fizyczny: Bibliogr. 13 poz.

Twórcy

autor

Datoussaid S.

autor

Guerlement G.

autor

Descamps T.

• Department of Civil Engineering and Structural Mechanics Faculte Polytechnique de Mons, Rue du Joncquois 53, 7000 Mons, Belgium Tel.: [phone]; fax: [phone],

Bibliografia

• 1. Haug EJ. and Arora J.S., Applied Optimal Design, John Wilcy & Sons, Chichester, UK(1979)
• 2. Ghaserni M. R. and Hinton E., A Genetic Search Based Arrangement of Load Combinations in Structural Analysis, Advances in Computational Structures Technology, Ed. Topping B.H.Y., pp 85-91, Civil-Comp Press (1996)
• 3. Ghasemi M. R. and Hinton E., Truss optimization using genetic algorithms, Advances in Structural Optimization, Ed. Topping B.H.Y., p 59-75, Civil-CompPress(1996)
• 4. Jenkins W.M., Structural Optimization with the Genetic Algońthm, The Structural Engineer, Yol. 64, pp 418-422 (1991)
• 5. Miyamura A., Kohama Y. and Takada T., Optimal Allocation ofShear Wall at 3D Frame by Genetic Algorithms, Advanccs in Structural Optimization for Structural Engineering, Ed. Topping B.H.Y., pp 73-79, Civil-Comp Press (1996)
• 6. Rajeev S. and Krishnamoorthy C.S., Discrete Optimization of Structures using Genetic Algorithms, Journal of Structural Engineering, Proceedings of theASCE, Vol. 118,pp 1233-1250, (1992)
• 7. Salajegheh K., Optimum design of piąte and shell Structures with discrete design variables, Advances in Structural Optimization, Ed. Topping B.H.Y. and Papadrakakis M., pp 187-193, Civil-Comp Press (1994)
• 8. Datoussai'd S., Optimisation du comportement dynamiąue et cinematiąue de systemes multicorps d structure cinematiąue complexe, PhD, Faculte Poly-techniąue de Mons (Belgium), (1999)
• 9. Franchi C. G., Migone F. and Toso A., Genetic Algorithms in Multi-link Front Suspension Optimization, lllh ADAMS European Users Conference, Mechanical Dynamics (1996)
• 10. DatoussaTd S., Optimal design of multibody systems by using genetic algo-rithms, Yehicle System Dynamics Supplement 28, pp 704-710 (1998)
• 11. Schittkowski K., Numerical Optimization - Theory, Methods and Applications, Nurnerical Analysis in Automotive Engineering'}, YDl-Berichte, Yol. 816, pp 191-202(1990)
• 12. Schwefel H.-P., Numerical Optimization of Computer Models, John Wiley, Chichester, UK( 1981)
• 13. Fogel D. B., Ań introduction to Simulated Evolutionary Optimization, IEEE Transactions on Neural Networks, Yol. 5, No l, pp. 3-14 (1994)