Journal of Global Optimization

, Volume 13, Issue 4, pp 433–444 | Cite as

Stochastic Methods for Practical Global Optimization

  • Zelda B. Zabinsky

Abstract

Engineering design problems often involve global optimization of functions that are supplied as ‘black box’ functions. These functions may be nonconvex, nondifferentiable and even discontinuous. In addition, the decision variables may be a combination of discrete and continuous variables. The functions are usually computationally expensive, and may involve finite element methods. An engineering example of this type of problem is to minimize the weight of a structure, while limiting strain to be below a certain threshold. This type of global optimization problem is very difficult to solve, yet design engineers must find some solution to their problem – even if it is a suboptimal one. Sometimes the most difficult part of the problem is finding any feasible solution. Stochastic methods, including sequential random search and simulated annealing, are finding many applications to this type of practical global optimization problem. Improving Hit-and-Run (IHR) is a sequential random search method that has been successfully used in several engineering design applications, such as the optimal design of composite structures. A motivation to IHR is discussed as well as several enhancements. The enhancements include allowing both continuous and discrete variables in the problem formulation. This has many practical advantages, because design variables often involve a mixture of continuous and discrete values. IHR and several variations have been applied to the composites design problem. Some of this practical experience is discussed.

Adaptive search Global optimization Multi-disciplinary optimization Simulated annealing 

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Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Zelda B. Zabinsky
    • 1
  1. 1.Industrial EngineeringUniversity of WashingtonSeattleUSA

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