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دانلود مقالات لاتین در زمینه الگوریتم ژنتیک ، شبکه های عصبی و منطق فازی رفتن به صفحه : قبلی  1, 2, 3 ... 13, 14, 15, 16  بعدی
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پست تاریخ: چهار‌شنبه 23 مهر 1393 - 21:50    عنوان:   پاسخگویی به این موضوع بهمراه نقل قول

A modified elitist genetic algorithm applied to the
design optimization of complex steel structures

María Belén Prendes Geroa,∗, Antonio Bello Garcíaa,
Juan José del Coz Díazb
aConstruction and Production Engineering Department, Oviedo University, Mieres Campus,
33600 Mieres, Spain
bConstruction and Production Engineering Department, Oviedo University,
Viesques Departmental Building no 6, 33204 Gijón, Spain
Received 9 January 2003; accepted 12 July 2004
Abstract
This paper shows the implementation of an elitist genetic algorithm that, when applied to steel
structures, is able to obtain structural elements with minimum weight and satisfy the safety factors or
coefficients (ultimate limit states) of the applicable building code. To this end, a modified objective
function has been defined that considers the constraints established by these coefficients. In addition,
the codification of the design variables has been modified so that all of them have the same probability
of initial selection; a selection operator has been implemented to consider the dispersion of the
individuals within the population as well as a crossover operator that interchanges the sections
assigned to the structural elements without their prior modification. The final result is a robust
genetic algorithm that is simple from a mathematical point of view and is able to work with complex
structures under different load and constraint conditions.
© 2004 Elsevier Ltd. All rights reserved.
Keywords: Genetic algorithm; Elitist; Steel structures; Minimum weight; Ultimate limit states

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پست تاریخ: چهار‌شنبه 23 مهر 1393 - 21:54    عنوان:   پاسخگویی به این موضوع بهمراه نقل قول

Optimum design of steel frames using a multiple-deme GA with improved
reproduction operators

D. Safari a, Mahmoud R. Maheria,∗, A. Maherib
a Shiraz University, Shiraz, Iran
b Northumbria University, UK
a r t i c l e i n f o
Article history:
Received 23 September 2010
Accepted 5 March 2011
Keywords:
Structural optimisation
Steel frames
Multiple-deme genetic algorithm
Modified crossover
Modified mutation
a b s t r a c t
In this paper, the performance of the genetic algorithm is improved by introducing some new crossover
and mutation operators. The new operators are incorporated into a multiple-deme genetic algorithm
in which population is divided into subpopulations and communication between different demes is
established through migration of individuals, enhancing diversity and resulting in better solutions.
This algorithm is applied to the minimum weight design of steel frames subjected to actual strength
and ductility constraints of AISC–ASD specifications as well as other serviceability and constructability
constraints. The efficiency of the proposed method is demonstrated through optimising two benchmark
problems including a three-bay, three-storey steel frame and a five-bay, 22-storey special steel frame.
Significant improvements in the optimum solutions are obtained with reduced number of finite element
analyses, resulting in less computational effort.

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پست تاریخ: چهار‌شنبه 23 مهر 1393 - 21:56    عنوان:   پاسخگویی به این موضوع بهمراه نقل قول

Optimal design of semi active control for adjacent buildings connected
by MR damper based on integrated fuzzy logic and multi-objective
genetic algorithm

Mehmet E. Uz, Muhammad N.S. Hadi ⇑
School of Civil, Mining & Environmental Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
a r t i c l e i n f o
Article history:
Received 3 June 2013
Revised 7 March 2014
Accepted 11 March 2014
Available online 12 April 2014
Keywords:
Adjacent buildings
Semi-active control
Clipped-optimal algorithm
Magneto-rheological (MR) damper
a b s t r a c t
An optimal design strategy based on genetic algorithms (GA) is proposed for nonlinear hysteretic control
devices that prevent pounding damage and achieve the best results in seismic response mitigation of two
adjacent structures. An integrated fuzzy controller is used in order to provide the interactive relationships
between damper forces and input voltages for MR dampers based on the modified Bouc-Wen
model. Furthermore, Linear Quadratic Regulator (LQR) and H2/LQG (Linear Quadratic Gaussian) controllers
based on clipped voltage law (CVL) are also used to compare the results obtained by fuzzy controller.
This study employs the main objectives of the optimal design that are not only to reduce the seismic
responses but also to minimize the total cost of the damper system. A set of Pareto optimal solutions
is also conducted with the corresponding results obtained from the optimal surface of Pareto solutions
in this study. As a result, decreasing the number of dampers does necessarily increase the efficiency of
the system. In fact, reducing the number of dampers for the dynamic response of the system can contribute
more than increasing the number of dampers.

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پست تاریخ: پنج‌شنبه 31 اردیبهشت 1394 - 23:00    عنوان:   پاسخگویی به این موضوع بهمراه نقل قول

NEURAL NETWORKS FOR DEFLECTIONS IN CONTINUOUS COMPOSITE BEAMS CONSIDERING CONCRETE CRACKING
Editorial, Volume 38, C1+, Winter 2014, Page 205-221 XML PDF (220 K)
Abstract
Maximum deflection in a beam is a design criteria and occurs generally at or close to
the mid-span. A methodology has been developed for continuous composite beams to predict the
inelastic mid-span deflections, d i (considering the cracking of concrete) from the elastic mid-span
deflections, d e (neglecting the cracking of concrete). Nine significant structural parameters have
been identified that govern the change in mid-span deflections. Six neural networks have been
presented to cover the entire practical range of the beams. The proposed neural networks have
been validated for a number of beams with different number of spans and the errors are small for
practical purposes. The methodology enables rapid estimation of inelastic deflections in
continuous composite beams and requires a computational effort that is a fraction of that required
for the conventional iterative or incremental analysis. The methodology can easily be extended for
large composite building frames where a huge savings in computational effort would result.
Keywords
Cracking; composite beam; deflection; neural networks; Sensitivity analysis
Source
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پست تاریخ: پنج‌شنبه 31 اردیبهشت 1394 - 23:42    عنوان:   پاسخگویی به این موضوع بهمراه نقل قول

OPTIMIZATION OF SKELETAL STRUCTURES USING A HYBRIDIZED ANT COLONY-HARMONY SEARCH- GENETIC ALGORITHM
Editorial, Volume 38, C1, Winter 2014, Page 1-20 XML PDF (401 K)
Abstract
Ant Colony Optimization (ACO) has been used as one of the popular meta-heuristic
algorithms in structural optimization. In this algorithm, the selected cross sections are chosen
according to a parameter called “probability ratio”. This parameter and the way to choose the cross
sections from a list of cross sections, are the most important points in the optimization process.
Though the Ant Colony algorithm has a special ability in achieving the optimal point, in some
cases in order to avoid local optima, the utilization of special techniques is needed. In the present
paper, the first aim is to use Harmony Search (HS) algorithm to increase the local search ability of
the ACO. In this way a combined algorithm, denoted by HACOHS, is obtained with special
abilities to achieve a global optimum. For this purpose, optimal design of skeletal structures such
as trusses and steel frames is considered using the HACOHS. However, in the process of
optimization by HACOHS method, several GA selections are employed at the cross section
selection stage. Utilizing the Tournament (HACOHS-T), Roulette wheel (HACOHS-Ro), and
Rank (HACOHS-Ra) methods it is found that the HACOHS-T is the most efficient of these
algorithms for optimal design of skeletal structures.
Keywords
optimization; ant colony algorithm; harmony search; genetic algorithm; skeletal structures
ُSource
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پست تاریخ: جمعه 1 خرداد 1394 - 18:49    عنوان:   پاسخگویی به این موضوع بهمراه نقل قول

ON THE PERFORMANCE OF A MODIFIED MULTIPLE-DEME GENETIC ALGORITHM IN LRFD DESIGN OF STEEL FRAMES
Editorial, Volume 37, Issue 2, Summer 2013, Page 169-190 XML PDF (438 K)
Abstract
This paper investigates the performance of a multiple-deme genetic algorithm (GA)
with modified reproduction operators, in optimal design of planar steel frames according to the
AISC-LRFD specification. The design objective is to minimise the weight of frame subject to
strength, displacement and constructability constraints. A number of new crossover and mutation
operators, used alongside the standard operators are utilised in optimum design of a number of
steel frames subjected to the constraints of the AISC-LRFD specification, with and without
considering the second order effects, as set out by the code requirements. This modified GA
(MGA) is shown to have a very fast convergence and to produce relatively high-quality designs.
This paper also utilizes the concept of multiple-deme in the GA, as it has been used successfully
for other metaheuristic population-based methods. The multiple-deme GA is used alongside the
modified GA operators and the algorithm is named the modified multiple-deme GA (MMDGA).
The modified GA (MGA) and modified multiple-deme GA (MMDGA) are applied to three
benchmark problems and the results are compared to those obtained by other metaheuristic
methods. In the majority of cases, the results of comparisons suggest the superiority of the
MMDGA in terms of the quality of final design and the total number of performed finite elements
analyses.
Keywords
Optimum design; multiple-deme genetic algorithm; steel frames; AISC-LRFD
Source
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پست تاریخ: جمعه 1 خرداد 1394 - 19:06    عنوان:   پاسخگویی به این موضوع بهمراه نقل قول

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTING COD REMOVAL EFFICIENCIES OF ROTATING DISKS AND PACKED-CAGE RBCS IN TREATING HYDROQUINONE
Editorial, Volume 37, Issue 2, Summer 2013, Page 325-326 XML PDF (417 K)
Abstract
In this study, an artificial neural network (ANN) was applied to predict the performance
of two rotating biological contactor (RBC) systems in removal of hydroquinone (a toxic aromatic
compound). The first system was a two-staged conventional RBC and the second one was a onestaged
packed-cage RBC with bee-cell 2000 biofilm carriers. Both systems had a total area of
about 2 m2 for biofilm attachment. The main aim is to predict COD removal efficiencies in both
systems using ANN. Efficiency evaluation of the reactors was obtained at different influent COD
from 200 to 5000 mg/L. Exploratory data analysis was used to detect relationships between the
data and the evaluated dependents. The appropriate architecture of the neural network models was
determined using several steps of training and testing the models. The modeling results showed
that there is a good agreement between the experimental data and the predicted values with a
correlation coefficient (R2) of 0.998 and 0.997 for RBC with rotating disks and packed-cage RBC,
respectively.
Keywords
Hydroquinone; COD; rotating biological contactor; neural networks
Source
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امتیاز: 210699
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پست تاریخ: شنبه 2 خرداد 1394 - 21:53    عنوان:   پاسخگویی به این موضوع بهمراه نقل قول

Optimal assignment of seismic vibration control actuators using genetic algorithm


Author(s): M. Abbasi , A. H. Davaei Markazi
Study Type: Research Paper | Subject: Structure-Earthquake
Article abstract:
An important factor in the design and implementation of structural control strategies is the number and placement of actuators. By employing optimally-located actuators, the effectiveness of control system increases, while with an optimal number of actuators, an acceptable level of performance can be achieved with fewer actuators. The method proposed in this paper, simultaneously determines the number and location of actuators, installed in a building, in an optimal sense. In particular, a genetic algorithm which minimizes a suitably defined structural damage index is introduced and applied to a well-known nonlinear model of a 20-story benchmark building. It is shown in the paper that an equal damage protection, compared to the work of other researchers, can be achieved with fewer numbers of optimally placed actuators. This result can be important from economic point of view. However, the attempt to minimize one performance index has negative effect on the others. To cope with this problem to some extent, the proposed genetic methodology has been modified to be applied in a multi-objective optimization problem.
Keywords: Structural control, Active control, Optimization, Methodology, Evolutionary algorithms, Benchmarks,
Full text [PDF 381 kb]
http://ijce.iust.ac.ir/browse.php?a_id=647&slc_lang=en&sid=1&ftxt=1
Reference:
http://ijce.iust.ac.ir/browse.php?a_id=647&sid=1&slc_lang=en

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پست: 37412

تشکر: 3350
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امتیاز: 210699
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پست تاریخ: شنبه 2 خرداد 1394 - 22:32    عنوان:   پاسخگویی به این موضوع بهمراه نقل قول

SA-IP method for congestion pricing based on level of service in urban network under fuzzy conditions


Author(s): S. Soudmand, M. Ghatee , S. M. Hashemi
Study Type: Research Paper | Subject: Transportation
Article abstract:
This paper proposes a new hybrid method namely SA-IP including simulated annealing and interior point algorithms to find the optimal toll prices based on level of service (LOS) in order to maximize the mobility in urban network. By considering six fuzzy LOS for flows, the tolls of congested links can be derived by a bi-level fuzzy programming problem. The objective function of the upper level problem is to minimize the difference between current LOS and desired LOS of links. In this level, to find optimal toll, a simulated annealing algorithm is used. The lower level problem is a fuzzy flow estimator model with fuzzy link costs. Applying a famous defuzzification function, a real-valued multi-commodity flow problem can be obtained. Then a polynomial time interior point algorithm is proposed to find the optimal solution regarding to the estimated flows. In pricing process, by imposing cost on some links with LOS F or E, users incline to use other links with better LOS and less cost. During the iteration of SA algorithm, the LOS of a lot of links gradually closes to their desired values and so the algorithm decreases the number of links with LOS worse than desirable LOS. Sioux Falls network is considered to illustrate the performance of SA-IP method on congestion pricing based on different LOS. In this pilot, after toll pricing, the number of links with LOS D, E and F are reduced and LOS of a great number of links becomes C. Also the value of objective function improves 65.97% after toll pricing process. It is shown optimal toll for considerable network is 5 dollar and by imposing higher toll, objective function will be worse.
Keywords: Congestion pricing, Level of service, Meta-heuristic, Fuzzy travel time, Multi-commodity, Interior point method,
Full text [PDF 361 kb]
http://ijce.iust.ac.ir/browse.php?a_id=800&slc_lang=en&sid=1&ftxt=1
Reference:
http://ijce.iust.ac.ir/browse.php?a_id=800&sid=1&slc_lang=en

_________________
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امتیاز: 210699
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پست تاریخ: یکشنبه 10 خرداد 1394 - 15:58    عنوان:   پاسخگویی به این موضوع بهمراه نقل قول

STRUCTURAL DAMAGE DETECTION BY MODEL UPDATING METHOD BASED ON CASCADE FEED-FORWARD NEURAL NETWORK AS AN EFFICIENT APPROXIMATION MECHANISM


Author(s): H. Fathnejat, P. Torkzadeh, E. Salajegheh * , R. Ghiasi
Study Type: Research | Subject: Optimal design | Received: 2014/11/17 - Accepted: 2014/11/17 - Published: 2014/11/17
Article abstract:
Vibration based techniques of structural damage detection using model updating method, are computationally expensive for large-scale structures. In this study, after locating precisely the eventual damage of a structure using modal strain energy based index (MSEBI), To efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, the MSEBI of structural elements is evaluated using properly trained cascade feed-forward neural network (CFNN). In order to achieve an appropriate artificial neural network (ANN) model for MSEBI evaluation, a set of feed-forward artificial neural networks which are more suitable for non-linear approximation, are trained. All of these neural networks are tested and the results demonstrate that the CFNN model with log-sigmoid hidden layer transfer function is the most suitable ANN model among these selected ANNs. Moreover, to increase damage severity detection accuracy, the optimization process of damage severity detection is carried out by particle swarm optimization (PSO) whose cost function is constructed based on MSEBI. To validate the proposed solution method, two structural examples with different number of members are presented. The results indicate that after determining the damage location, the proposed solution method for damage severity detection leads to significant reduction of computational time compared to finite element method. Furthermore, engaging PSO algorithm by efficient approximation mechanism of finite element (FE) model, maintains the acceptable accuracy of damage severity detection.
Keywords: damage detection, approximation mechanism, cascade feed-forward neural network, modal strain energy based index, particle swarm optimization.,
Reference
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