Stochastic Search Methods for Global Optimization

Zabinsky, Zelda B.

doi:10.1002/9780470400531.eorms1009

Cited by 7 publications

(3 citation statements)

References 59 publications

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“…Another program for the design of buckled stiffened panels is PANDA2 (Bushnell 1987) that can analyse panels using FSM, finite difference energy method or smeared representation of the stiffeners. VICONOPT and PANDA2 use the method of feasible directions for carrying out the optimization while COSTADE uses the Improving Hit-and-Run algorithm (Zabinsky 1998).…”

Section: Introductionmentioning

confidence: 99%

Robust design and optimization of composite stiffened panels in post-buckling

Bacarreza

Aliabadi

Apicella

2014

Struct Multidisc Optim

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Multilevel optimization including progressive failure analysis and robust design optimization for composite stiffened panels, in which the ultimate load that a post-buckled panel can bear is maximized for a chosen weight, is presented for the first time. This method is a novel robust multiobjective approach for structural sizing of composite stiffened panels at different design stages. The approach is integrated at two design stages labelled as preliminary design and detailed design. The robust multilevel design methodology integrates the structural sizing to minimize the variance of the structural response. This method improves the product quality by minimizing variability of the output performance function. This innovative approach simulates the sequence of actions taken during design and structural sizing in industry where the manufacture of the final product uses an industrial organization that goes from the material characterization up to trade constraints, through preliminary analysis and detailed design. The developed methodology is validated with an example in which the initial architecture is conceived at the preliminary design stage by generating a Pareto front for competing objectives that is used to choose a design with a required weight. Then a robust solution is sought in the neighbourhood of this solution to finally find the layup for the panel capable of bearing the highest load for the given geometry and boundary conditions.

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Section: Introductionmentioning

confidence: 99%

Robust design and optimization of composite stiffened panels in post-buckling

Bacarreza

Aliabadi

Apicella

2014

Struct Multidisc Optim

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“…For specific combinatorial problems, such as the Traveling Salesman Problem, specialized neighborhood generators have been developed to be used in random sampling in optimization algorithms [3,10,27]. However, for mixed continuous/discrete domains, which are prevalent in many engineering optimization problems [12,18,22,28], appropriate neighborhood generators have not been developed. Many effective global and convex optimization algorithms for continuous problems have embedded the Markov chain Monte Carlo sampler known as Hit-and-Run (HR) [7,11,19,20,29,30].…”

Section: Introductionmentioning

confidence: 99%

Pattern discrete and mixed Hit-and-Run for global optimization

et al. 2010

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We develop new Markov chain Monte Carlo samplers for neighborhood generation in global optimization algorithms based on Hit-and-Run. The success of Hit-and-Run as a sampler on continuous domains motivated Discrete Hit-and-Run with random biwalk for discrete domains. However, the potential in efficiencies in the implementation, which requires a randomization at each move to create the biwalk, lead us to a different approach that uses fixed patterns in generating the biwalks. We define Sphere and Box Biwalks that are pattern-based and easily implemented for discrete and mixed continuous/discrete domains. The pattern-based Hit-and-Run Markov chains preserve the convergence properties of Hitand-Run to a target distribution. They also converge to continuous Hit-and-Run as the mesh of the discretized variables becomes finer, approaching a continuum. Moreover, we provide bounds on the finite time performance for the discrete cases of Sphere and Box Biwalks. We embed our samplers in an Improving Hit-and-Run global optimization algorithm and test their performance on a number of global optimization test problems.

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“…Stochastic approaches, such as simulated annealing, genetic algorithms, and evolutionary programming, are useful for optimizing engineering problems. The reader may consult [2,5,8,9,11,29,31,33] for more details of various techniques on the stochastic approaches.…”

Section: Optimization Algorithm Considerationmentioning

confidence: 99%

A Case Study: Composite Structure Design Optimization

Zabinsky

Tuttle

Khompatraporn

Nonconvex Optimization and Its Applications

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This chapter presents an engineering apphcation of global optimization with a case study on designing large composite structures. The problem of designing large composite structures often becomes a global optimization problem when practical issues such as performance and manufacturabihty of the structures are considered. We demonstrate how problems on this type can be formulated and solved using two design examples. A stochastic optimization approach is used to obtain the solutions in both examples.

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Stochastic Search Methods for Global Optimization

Cited by 7 publications

References 59 publications

Robust design and optimization of composite stiffened panels in post-buckling

Robust design and optimization of composite stiffened panels in post-buckling

Pattern discrete and mixed Hit-and-Run for global optimization

A Case Study: Composite Structure Design Optimization

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