Download Discrete Structural Optimization by W. Gutkowski (eds.) PDF

By W. Gutkowski (eds.)

The engineering layout of buildings and machines is composed usually find the simplest answer between a finite variety of possible judgements. This quantity contains difficulties and answer equipment for discrete structural optimization. specific, approximate and heuristic equipment are provided using deterministic and stochastic approaches.

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Extra resources for Discrete Structural Optimization

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Schafer, J. Geilen and H. A. uer, Application of Discrete Optimization Techniques to Optimal Composite Structures, 61-70. 8. A. Osyczka and J. Montusiewicz, A Random Search Approach to Multicriterion Discrete Optimization, 71-79. 9. A. 0. zov and A. S. Dekhtjar, Optimal Arrangement of Ribs in Rib Reinforced Plates and Shells, 80-87. 10. J. Niczyj and W. Paczkowski, Application of the Expert System for Discrete Optimization of Space Truss, 88-97. 11. V. V. Toropov, V. L. Markin and H. Carlsen, Discrete Structural Optimization Based on Multipoint Explicit Approximations, 98-107.

L. , Proc. of the tenth ASCE Conference on Electronic Computation, Indianapolis, Indiana ASCE, 1991, 173-180. 53 Chapter 2 STOCHASTIC SEARCH IN DISCRETE STRUCTURAL OPTIMIZATION SIMULATED ANNEALING, GENETIC ALGORITHMS AND NEURAL NETWORKS P. Hajela Rensselaer Polytechnic Institute, Troy, NY, USA ABSTRACT A number of structural optimization problems are characterized by the presence of discrete and integer design variables, over and beyond the more traditional continuous variable problems. In some applications, the number of design variables may be quite large.

The use of conventional mathematical programming methods in such problems is fraught with hazards. First, these gradientbased methods cannot be used directly in the presence of discrete variables. Their use is facilitated by creating multiple equivalent continuous variable problems (branch and bound techniques); in the presence of high-dimensionality, the number of branched problems that may have to be solved can be quite large. In some problems, the analysis can be computationally demanding, thereby further limiting the effective use of these methods.

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