A comparison of search mechanisms for structural optimization

Manoharan, Sathiamoorthy; Shanmuganathan, Subana

doi:10.1016/s0045-7949(98)00287-9

Cited by 45 publications

(15 citation statements)

References 10 publications

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“…Augugliaro et al (1999) compare SA, GA, and TS in problems involving the reconfiguration of radial distribution electric power networks and find that TS performs best in this class of problems. Finally, Fu & Su (2000), Fudo et al (2000), Ganley & Heath (1998), Manoharan & Shanmuganathan (1999), Sinclair (1993), and Youssef et al (2001) compare SA, GA, and TS in problems involving the minimization of assembly time in printed wiring assembly, service restoration in electricity distribution systems, graph partitioning, structural engineering optimization, balancing hydraulic turbine runners, and the floor-planning of very large scale integrated (VLSI) circuits, respectively, in many cases without identifying any clear winners.…”

Section: Discussionmentioning

confidence: 99%

Stochastic Optimization: a Review

Fouskakis

Draper

2002

Int Statistical Rev

145

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We review three leading stochastic optimization methods-simulated annealing, genetic algorithms, and tabu search. In each case we analyze the method, give the exact algorithm, detail advantages and disadvantages, and summarize the literature on optimal values of the inputs. As a motivating example we describe the solution-using Bayesian decision theory, via maximization of expected utility-of a variable selection problem in generalized linear models, which arises in the cost-effective construction of a patient sickness-at-admission scale as part of an effort to measure quality of hospital care.

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Section: Discussionmentioning

confidence: 99%

Stochastic Optimization: a Review

Fouskakis

Draper

2002

Int Statistical Rev

145

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“…In this study, TS algorithm is selected because of its efficiency in solving combinatorial optimization problems [52][53][54]. TS algorithm has been applied to various structural optimization problems and has been found very effective in identifying the optimal solutions [28,55,56].…”

Section: Evaluation Of Tabu Search Algorithmmentioning

confidence: 99%

Life‐cycle cost assessment of RC and ECC frames using structural optimization

Gencturk

2012

Earthq Engng Struct Dyn

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SUMMARY In this study life‐cycle cost (LCC) assessment of structural frames is performed. Two different materials, reinforced concrete (RC) and reinforced engineered cementitious composites (ECC), with different response characteristics are used to model the frames. ECC is characterized by high tensile ductility and energy absorption and reduced crack widths when compared to conventional concrete. However, the material is more expensive than conventional concrete; therefore, in order to quantify the potential benefits that could be obtained by replacing concrete with ECC, the life‐cycle performance is evaluated in an optimization framework. Three different structural frames are considered: an RC only frame, an ECC only frame and a multi‐material (MX) frame in which ECC is selectively applied at the potential plastic hinge locations while the remainder of the frame is made of RC. The structural capacity and earthquake demand are evaluated using rigorous analysis methods to capitalize on different characteristics of concrete and ECC, and both aleatory and epistemic uncertainties are incorporated into the LCC formulation. It is found that both the initial and LCC of frames that use ECC are lower due to savings in material and labor cost of transverse reinforcement for the former and due to increased capacity and reduced demand for the latter. Copyright © 2012 John Wiley & Sons, Ltd.

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“…Sequential Quadratic Programming (SQP) [40] was used for this purpose via matlab function fmincon. The SQP method is relatively efficient for high dimensional problems (genetic algorithms, and simulated annealing by contrast, appear to be rather less efficient [41,42]). In addition to imposed parameter constraints, the need for an implicit penalty constraint was mentioned in Section 5 to allow calibration.…”

Section: Physical Model Calibrationmentioning

confidence: 99%

NARX Neural Network Modelling of Hydraulic Suspension Dampers for Steady-state and Variable Temperature Operation

Patel¹,

Dunne²

2003

Vehicle System Dynamics

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Two NARX-type neural networks are developed for modelling nonlinear dynamic characteristics of passive twin-tube hydraulic dampers used in vehicle suspension systems. Quasi-isothermal and variable temperature NARX models are rigorously tested and compared with a state-of-the-art physical model proposed by Reybrouck (1998) andDuym (2000). Measured damper data, generated under isothermal and temperature varying conditions, is used for NARX training, physical model calibration, and predictive comparisons. Test kinematics include high amplitude sinusoidal displacements up to 14 Hz, and realistic random road profiles. The NARX models are trained via 'teacher forcing' and the feedforward backpropagation algorithm using both 'Early Stopping' and Bayesian Regularisation. Stable network design is also examined using the minimum posterior prediction error as the criterion for selecting a good network from a small number of tests. Calibration of the physical model proves highly complicated owing to considerable nonlinearity-in-the-parameters, requiring use of Sequential Quadratic Programming with an implicitly nonlinear constraint. The paper shows that NARX neural network modelling is vastly superior in terms of calibration efficiency, and prediction times, whilst offering roughly similar, if not better, model accuracy.

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A comparison of search mechanisms for structural optimization

Cited by 45 publications

References 10 publications

Stochastic Optimization: a Review

Stochastic Optimization: a Review

Life‐cycle cost assessment of RC and ECC frames using structural optimization

NARX Neural Network Modelling of Hydraulic Suspension Dampers for Steady-state and Variable Temperature Operation

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