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Generalized Hopfield network based structural optimization using sequential unconstrained minimization technique with additional penalty strategy

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ADVANCES IN ENGINEERING SOFTWARE 2002年第7-10期33卷 721-729页

作者： Shih, CJ Yang, YC Tamkang Univ Dept Mech Engn Tamsui 25137 Taiwan

This paper presents and examines a neuron-like framework of the generalized Hopfield network (GHN) that is capable to solve nonlinear engineering optimization problems with mixed discrete, integer and real continuous variables. The sequential unconstrained minimization technique (SUMT) was applied to construct the GHN for dealing with the design constraints. An additional penalty function for dealing with the discrete and integer variables was then imposed on the formulation of SUMT to construct an energy function of GHN for formulating the neuron-like dynamical system. The numerical solution process for such a dynamic system is simply solving a set of simultaneous first-order ordinary differential equations (ODE) that is the main feature of this optimization method. The experimental examples showed the presenting strategy is reliable. The suitable values or the adaptation technique for some parameters in computation was discussed in the paper. The presenting strategy indeed provides an alternative way of handling the engineering optimization dynamically and expands the usage of ODE. An asymmetrical three-bar truss design, a reinforced concrete beam design and a 10-bar structural design are contributed to illustrate the presenting neuron-like network method. (C) 2002 Civil-Comp Ltd and Elsevier Science Ltd. All rights reserved.

关键词： generalized Hopfield network nonlinear programming sequential unconstrained minimization technique penalty function ordinary differential equation structural optimization

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Generalized Hopfield network based structural optimization using sequential unconstrained minimization technique with additional penalty strategy

Generalized Hopfield network based structural optimization u...

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2nd International Conference on Engineering Computational Technology/5th International Conference on Computational Structures Technology

作者： Shih, CJ Yang, YC Tamkang Univ Dept Mech Engn Tamsui 25137 Taiwan

关键词： generalized Hopfield network nonlinear programming sequential unconstrained minimization technique penalty function ordinary differential equation structural optimization

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Collective control in arrays of wave energy converters

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RENEWABLE ENERGY 2020年 156卷 361-369页

作者： Zou, Shangyan Abdelkhalik, Ossama Iowa State Univ Dept Aerosp Engn Ames IA 50011 USA

A Collective Control is developed in this paper for arrays of Wave Energy Converters (WECs). The proposed controller applies a Proportional-Derivative feedback control law for each WEC with the optimized controller gains. A surrogate model, composed of only mechanical elements, is adopted to replace the hydrodynamic model during the optimization process. An indirect exterior penalty function approach is implemented to handle the constraints on the displacement and control. The weight of the penalty function is updated in subsequent iterations in the sequential unconstrained minimization technique. A numerical simulation is first conducted for identification of the surrogate model parameters. With the control gains optimized based on the surrogate model, the energy conversion is compared for three models: the surrogate model, a model that uses a boundary element tool to compute the hydrodynamic forces, and a simulation using AQWA. The results show a good agreement of the energy conversion among the models. Finally, the performance of the surrogate model is analyzed. It is shown that the proposed controller maximizes the energy conversion of the entire WEC array while satisfying the constraints. Moreover, the surrogate model can replace the hydrodynamic model to predict the system behavior with adequate accuracy and more efficiency. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Wave energy conversion Wave energy converters array Collective control Surrogate model sequential unconstrained minimization technique

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Estimation of Border-Strip Soil Hydraulic Parameters

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JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2012年第6期138卷 493-502页

作者： Ram, Shobha Prasad, K. S. Hari Gairola, Ajai Jose, M. K. Trivedi, M. K. Indian Inst Technol Dept Civil Engn Roorkee 247667 Uttarakhand India Natl Inst Hydrol Roorkee 247667 Uttarakhand India Reg Ctr BELGAUM Natl Inst Hydrol Belgaum 590001 Karnataka India Madhav Inst Sci & Technol Dept Civil Engn Gwalior Madhya Pradesh India

The inverse problem of determining soil hydraulic parameters (saturated hydraulic conductivity and water retention parameters) of border-strip irrigation from irrigation event data is analyzed. The inverse problem is solved using sequential unconstrained minimization technique. The forward problem involves the solution of coupled Saint-Venant's equation governing overland flow and Richard's equation governing subsurface flow. Saint-Venant's equations are solved using the MacCormack scheme-based finite-difference method while Richard's equation is solved using a mass conservative fully implicit finite-difference method. Field experiments are conducted on two border strips to obtain surface and subsurface irrigation data such as irrigation advance, recession, flow depth, and soil moisture content. The soil hydraulic parameters, i.e., saturated hydraulic conductivity and soil retention parameters, are estimated by minimizing the deviations between the model-predicted and field-observed irrigation data. The results indicate that defining the objective function in terms of flow depths results in the optimization algorithm converging to the true values as compared to the use of irrigation advance data. Further, it is observed that underestimating the initial guess results in the least number of iterations for the optimization algorithm to converge to the true values. It is also observed that simultaneous estimation of all three soil hydraulic parameters is not possible even with the inclusion of subsurface moisture content data in the objective function. DOI: 10.1061/(ASCE)IR.1943-4774.0000398. (C) 2012 American Society of Civil Engineers.

关键词： Border-strip irrigation Identifiability Parameter estimation Least-squares minimization sequential unconstrained minimization technique

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Optimization-based synthesis of a deep-digging tillage mechanism

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2000年第1A期24卷 61-78页

作者： Nokleby, SB Podhorodeski, RP Univ Victoria Dept Mech Engn Robot & Mech Lab Victoria BC V8W 3P6 Canada

A quasi-Newton optimization method is employed to synthesize a four-bar tillage mechanism, a device for loosening the sub-soil in farm fields. The synthesis routine uses sequential parameter transformations that map from an unconstrained search variable space to a constrained design variable space. The sequential parameter transformations ensure that only a specific mechanism sub-type is considered, that Grashof criteria are satisfied, and that ail mechanism parameters satisfy specified upper and lower constraints. Objective functions quantifying the level of satisfaction of the desired task displacements are minimized. It is shown that in addition to task satisfaction, further objective function terms can be added. The addition of objective function terms to increase the minimum transmission angle and to reduce the mechanism size are found to allow the synthesis of a practical mechanism for the deep-digging application. The use of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method with Fletcher's Line Search (FLS) algorithm has allowed for the development of an efficient optimization-based synthesis routine. The synthesis results demonstrate that the developed synthesis routine required on the order of 10(2) fewer iterations per start then the direct-search and sequential unconstrained minimization technique (SUMT) employed in a previous method.

关键词： synthesis Satisfaction Objective function sequential unconstrained minimization technique optimization method tillage

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Shape optimization of structures for frequency constraints by sequential harmony search algorithm

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ENGINEERING OPTIMIZATION 2013年第6期45卷 627-646页

作者： Gholizadeh, S. Barzegar, A. Urmia Univ Dept Civil Engn Orumiyeh Iran

Shape optimization of structures with frequency constraints is a highly nonlinear dynamic optimization problem. In order to deal with this type of optimization problem, efficient optimization algorithms should be employed. The main objective of the present study is to propose an efficient harmony search (HS)-based algorithm for solving the shape optimization problem of pin-jointed structures subject to multiple natural frequency constraints. In the proposed algorithm an enhanced version of HS is employed in the framework of the sequential unconstrained minimization technique. The efficiency of the presented sequential harmony search (SHS) algorithm is illustrated through several benchmark optimization examples and the results are compared to those of different optimization techniques. The numerical results demonstrate the computational advantages of the SHS in shape optimization of structures for frequency constraints.

关键词： shape optimization harmony search algorithm frequency constraint sequential unconstrained minimization technique

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Estimation of water cloud model vegetation parameters using a genetic algorithm

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2012年第4期57卷 776-789页

作者： Kumar, Kamal Prasad, K. S. Hari Arora, M. K. Indian Inst Technol Roorkee Dept Civil Engn Roorkee 247667 Uttar Pradesh India

The water cloud model is used to account for the effect of vegetation water content on radar backscatter data. The model generally comprises two parameters that characterize the vegetated terrain, A and B, and two bare soil parameters, C and D. In the present study, parameters A and B were estimated using a genetic algorithm (GA) optimization technique and compared with estimates obtained by the sequential unconstrained minimization technique (SUMT) from measured backscatter data. The parameter estimation was formulated as a least squares optimization problem by minimizing the deviations between the backscatter coefficients retrieved from the ENVISAT ASAR image and those predicted by the water cloud model. The bias induced by three different objective functions was statistically analysed by generating synthetic backscatter data. It was observed that, when the backscatter coefficient data contain no errors, the objective functions do not induce any bias in the parameter estimation and the true parameters are uniquely identified. However, in the presence of noise, these objective functions induce bias in the parameter estimates. For the cases considered, the objective function based on the sum of squares of normalized deviations with respect to the computed backscatter coefficient resulted in the best possible estimates. A comparison of the GA technique with the SUMT was undertaken in estimating the water cloud model parameters. For the case considered, the GA technique performed better than the SUMT in parameter estimation, where the root mean squared error obtained from the GA was about half of that obtained by the SUMT.

关键词： vegetation parameter backscatter coefficient inverse problem genetic algorithm data errors bias root mean squared error sequential unconstrained minimization technique

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On long step path following and SUMT for linear and quadratic programming

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SIAM JOURNAL ON OPTIMIZATION 1996年第1期6卷 33-46页

作者： Anstreicher, KM GEORGE WASHINGTON UNIV DEPT OPERAT RESWASHINGTONDC

We consider a long step barrier algorithm for the minimization of a convex quadratic objective subject to linear inequality constraints. The algorithm is a dual version of a method developed by Anstreicher et al. [Algorithmica, 10 (1993), pp. 365-382], and requires O(nL) or O(root nL) iterations to solve a problem with n constraints, depending on how the barrier parameter is reduced. As a corollary of our analysis we demonstrate that the classical SUMT algorithm, exactly as implemented in 1968, solves linear and quadratic programs in O(root nL log L) iterations, with proper initialization and choice of parameters.

关键词： linear programming quadratic programming barrier function long step path following sequential unconstrained minimization technique

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A CLASS OF GENERALIZED VARIABLE PENALTY METHODS FOR NON-LINEAR PROGRAMMING

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1981年第2期35卷 159-182页

作者： PRASAD, B Senior Research Scientist Sceintific Research Lab. Engineering and Research Staff Ford Motor Company Dearborn Michigan

A class of generalized variable penalty formulations for solving nonlinear programming problems is presented. The method poses a sequence of unconstrained optimization problems with mechanisms to control the quality of the approximation for the Hessian matrix, which is expressed in terms of the constraint functions and their first derivatives. The unconstrained problems are solved using a modified Newton's algorithm. The method is particularly applicable to solution techniques where an approximate analysis step has to be used (e.g., constraint approximations, etc.), which often results in the violation of the constraints. The generalized penalty formulation contains two floating parameters, which are used to meet the penalty requirements and to control the errors in the approximation of the Hessian matrix. A third parameter is used to vary the class of standard barrier or quasibarrier functions, forming a branch of the variable penalty formulation. Several possibilities for choosing such floating parameters are discussed. The numerical effectiveness of this algorithm is demonstrated on a relatively large set of test examples.

关键词： Variable penalty methods nonlinear programming sequential unconstrained minimization technique approximations Hessian matrix penalty methods ill-conditioning

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Optimization of Rolling Schedule in Tandem Cold Mill Based on QPSO Algorithm

Optimization of Rolling Schedule in Tandem Cold Mill Based o...

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International Symposium on Advanced Rolling Equipment and Technologies

作者： Lin, Sen Nan, Yurong Zhejiang Univ Technol Hangzhou 310014 Zhejiang Peoples R China

ISBN: (纸本)9780878492190

This paper adopts equal relatively load as objective function, and makes every parameter to meet certain restrictive conditions. SUMT algorithm was used to change constraints to non-binding conditions. QPSO algorithm was applied to optimize objective functions to obtain optimal solution. This algorithm was based on classical particle swarm optimization, which, with the conduct of quantum particle, had effective global search capability, good convergence and stability. As a result, reasonable distribution of tandem cold rolling power and full use of equipment capacity were realized, resulting in the improvement of production efficiency.

关键词： Tandem cold rolling Schedule optimization Quantum particle swarm optimization sequential unconstrained minimization technique

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