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Paper III.1

Mixed Discrete and Integer Structural Optimization using Generalized Hopfield Network based Sequential Unconstrained Mimimization Technique with Additional Penalty Strategy

doi:10.4203/ccp.72.3.1

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C.J. Shih, Y.C. Yang, "Mixed Discrete and Integer Structural Optimization using Generalized Hopfield Network based Sequential Unconstrained Mimimization Technique with Additional Penalty Strategy", in G. De Roeck, B.H.V. Topping, (Editors), "Identification, Control and Optimisation of Engineering Structures", Civil-Comp Press, Edinburgh, UK, pp 73-78, 2000. doi:10.4203/ccp.72.3.1

Abstract

This paper presents and examines a neuron-like framework named the generalized Hopfield network (GHN) that is capable to solve nonlinear optimization problems with mixed discrete, integer and real continuous variables. The sequential unstrained minimization technique (SUMT) was applied to transform a constrained problem to an unconstrained formulation. An additional penalty function for dealing with discrete and integer variables was then imposed on the formulation of SUMT to construct an energy function for formulating the neuron-like dynamical system. The numerical solution process for such a dynamic system is nothing but solving a set of simultaneously first-order ordinary differential equations. The experimental examples showed the presenting strategy is efficient and reliable. The suitable values or the adaptation technique for some parameters in computation was discussed in the paper. The presenting strategy provides another mode of handling die engineering optimization dynamically. This alternative optimization approach also extends the limit of numerical structural engineering optimization.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 72 IDENTIFICATION, CONTROL AND OPTIMISATION OF ENGINEERING STRUCTURES Edited by: G. De Roeck and B.H.V. Topping Paper III.1 Mixed Discrete and Integer Structural Optimization using Generalized Hopfield Network based Sequential Unconstrained Mimimization Technique with Additional Penalty Strategy C.J. Shih and Y.C. Yang Department of Mechanical Engineering, Tamkang University, Tamsui, Taiwan doi:10.4203/ccp.72.3.1 purchase the full-text of this paper Full Bibliographic Reference for this paper C.J. Shih, Y.C. Yang, "Mixed Discrete and Integer Structural Optimization using Generalized Hopfield Network based Sequential Unconstrained Mimimization Technique with Additional Penalty Strategy", in G. De Roeck, B.H.V. Topping, (Editors), "Identification, Control and Optimisation of Engineering Structures", Civil-Comp Press, Edinburgh, UK, pp 73-78, 2000. doi:10.4203/ccp.72.3.1 Abstract This paper presents and examines a neuron-like framework named the generalized Hopfield network (GHN) that is capable to solve nonlinear optimization problems with mixed discrete, integer and real continuous variables. The sequential unstrained minimization technique (SUMT) was applied to transform a constrained problem to an unconstrained formulation. An additional penalty function for dealing with discrete and integer variables was then imposed on the formulation of SUMT to construct an energy function for formulating the neuron-like dynamical system. The numerical solution process for such a dynamic system is nothing but solving a set of simultaneously first-order ordinary differential equations. The experimental examples showed the presenting strategy is efficient and reliable. The suitable values or the adaptation technique for some parameters in computation was discussed in the paper. The presenting strategy provides another mode of handling die engineering optimization dynamically. This alternative optimization approach also extends the limit of numerical structural engineering optimization. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £50 +P&P)
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