Design Optimization

Design Optimizationprovides engineers who design structures with an easy-to-use tool based on iterative methods. Using Design Optimization is mainly a question of practice and methodology.

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Optimization plays a prominent role in structural design. The importance of minimum weight design of structures is recognized in most industries because the weight of the system affects its performance or because of the depletion of our conventional energy sources. But optimization is not only a matter of weight, it can be used to optimize any type of data. In real world engineering problems, it is also common to minimize an objective function describing data such as the total volume, the life-time or the cost of a structure.

The Design Optimization app can operate with two types of algorithms: Local algorithms (Conjugate Gradient) and a global algorithm (Simulated Annealing). You select one or the other to run an optimization depending on the function to analyze. Although it is not required to have a prior preparation in optimization techniques, those of you who want to know in detail how these algorithms work can refer to the publications below:

  • Numerical Recipes in C - The Art of Scientific Computing - ISBN - 0 - 521 - 43108 - 5, 1988 - 1992
  • Non convex Minimization Calculations and the Conjugate Gradient Method, Powell, M.J.D., Lecture Notes in Mathematics, Vol. 1066, pp. 122-141, 1984. [Advanced review on conjugate gradient for non convex functions.]
  • Optimization by Simulated Annealings, Randelman, R. E., and Grest, G.S., N-City Traveling Salesman Problem - , J.Stat. Phys. 45, 885-890, 1986.
  • Function Minimization by Conjugate Gradients, Fletcher, R. and Reeves, C.M., Comp, J. 7, 149-154, 1964. [Advanced article on using conjugate gradient for non convex functions.]