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Journal ArticleDOI

Stochastic Methods for Practical Global Optimization

Zelda B. Zabinsky
- 01 Dec 1998 - 
- Vol. 13, Iss: 4, pp 433-444
TLDR
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, and several variations have been applied to the composites design problem.
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.

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Citations
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Journal ArticleDOI

Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels

TL;DR: The extensive use of the uncertainty information of predictions for screening the candidate solutions makes it possible to significantly reduce the computational cost of singleand multiobjective EA.

Global optimization and simulated annealing

TL;DR: The mathematical formulation of the simulated annealing algorithm is extended to continuous optimization problems, and it is proved asymptotic convergence to the set of global optima.
Proceedings ArticleDOI

A recursive random search algorithm for large-scale network parameter configuration

TL;DR: A new heuristic search algorithm, Recursive Random Search (RRS), for large-scale network parameter optimization, based on the initial high-efficiency feature of random sampling, which has been applied to the configuration of several network protocols, such as RED, OSPF and BGP.
Journal ArticleDOI

Efficient Differential Evolution algorithms for multimodal optimal control problems

TL;DR: The results show that within the class of evolutionary methods, Differential Evolution algorithms are very robust, effective and highly efficient in solving the studied class of optimal control problems and are able of mitigating the drawback of long computation times commonly associated with Evolutionary algorithms.
Book ChapterDOI

Exact Algorithms for Global Optimization of Mixed-Integer Nonlinear Programs

TL;DR: It is demonstrated that practically relevant nonlinear programs can be solved to global optimality in a completely automated fashion when carefully chosen relaxation schemes, branching strategies, and domain reduction techniques are used in conjunction with branch to enhance its performance.
References
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