Multi-criteria material selection in engineering design

Almost all material selection problems require that a compromise be sought between some metric of performance and cost. Trade-off methods using utility functions allow optimal solutions to be found for two objective, but for three it is harder. This paper develops and demonstrates a method for dealing with three objectives.

Shear-flexible subdivision shells

We present a new shell model and an accompanying discretisation scheme that is suitable for thin and thick shells. The deformed configuration of the shell is parameterised using the mid-surface position vector and an additional shear vector for describing the out-of-plane shear deformations. In the limit of vanishing thickness, the shear vector is identically zero and the Kirchhoff-Love model is recovered. Importantly, there are no compatibility constraints to be satisfied by the shape functions used for discretising the mid-surface and the shear vector. The mid-surface has to be interpolated with smooth C 1-continuous shape functions, whereas the shear vector can be interpolated with C 0-continuous shape functions. In the present paper, the mid-surface as well as the shear vector are interpolated with smooth subdivision shape functions. The resulting finite elements are suitable for thin and thick shells and do not exhibit shear locking. The good performance of the proposed formulation is demonstrated with a number of linear and geometrically non-linear plate and shell examples. © 2012 John Wiley & Sons, Ltd.

Rigorous finite-time guarantees for optimization on continuous domains by simulated annealing

Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.

Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains

Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.