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1 Foundations of Civil and Environmental Engineering

1 2006

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Applications of evolutionary strategies in optimal design of mechanical systems

100%

Datoussaid S. , Guerlement G. , Descamps T.

tom No. 7

35-51

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.