In this research, a hybrid system was proposed between the
genetic algorithm and the fuzzy Kohonen clustering network ,
where the genetic algorithm is one of the methods of artificial
intelligence is one of the modern methods.
In this paper, it has
merged two techniques of the artificial intelligent, they are the
ants colony optimization algorithm and the genetic algorithm, to
The recurrent reinforcement learning trading system
optimization. The proposed trading system
is based on an ant
colony optimization algorithm and the genetic algorithm to
select an optimal group of technical indicators, and fundamental
indicators.
The main objective of this research is to present a study on the design optimization of the 6-RUS Stewart platform.
The geometric and kinematic models are calculated and the singular positions are determined, then its translation and orientation wor
kspace are determining. The direct geometric model of the studied platform was determined by using a proposed hybrid method.
Thin walled Steel products are very much used in the construction industry, where it
is cold formed from uniform thickness steel plates. This study aims at determining the
optimal section of cold formed thin walled lipped C compressed member under
the effect
of several levels of axial force using Genetic Algorithm.
The research found that the genetic algorithm is able to resolve the issue of the
optimal design of studied column with high efficiency, accuracy. Also it found that the
torsional flexural buckling constraint and the overall buckling constraint in x-direction are
the effective constraints in case of long height.
The study recommends restudying the same issue as a multi objective optimization
problem by adding additional objective functions which are the overall buckling in x&y
directions.
In the Multi-objective Traveling Salesman Problem (moTSP)
simultaneous optimization of more than one objective functions is
required. This paper proposes hybrid algorithm to solve the multiobjectives
Traveling Salesman problem through the integration of
the ant colony optimization algorithm with the Genetic algorithm.
This paper aims to reduce the power losses and to enhance the
voltage profile of the power system while maintaining the loading of the
transmission lines within the allowable limits, through the optimal
placement of the Unified Power Flow Controller (UPFC).
Structural design for seismic loading, which is traditionally done for most types of common structures by the means of equivalent lateral static loading or modal spectrum analysis, is no longer a preferred methodology for design of modern structures
with complex topology and functionality under extreme loading scenarios. Nonlinear response history evaluation, on the other hand, is becoming a practical tool due to availability of high performance computing and recommendations of the new seismic guidelines, and due to the increase of available strong ground motion database. Therefor using and scaling real recorded accelerograms is becoming one of the most contemporary research issues in this field.
Seismological characteristics of the records, such as earthquake magnitude, epicentral distance and site classification are usually considered in the selection of real records, as they influence the shape of the response spectrum, the energy content and duration of strong ground shaking, and therefore the expected demand on structures. After real seismic records selection it is necessary to scale these records to match the intensity of the earthquake expected for the site. Generally, scaling can be made by ground motions uniform scaling in time domain which is simply scaled up or down the ground motions uniformly to best match (in average) the target spectrum within a period range of interest. It’s an engineer’s job to find the best scaling factors to best match the target spectrum, which is a complex task, so we employed the Genetic Algorithm (GA) in finding them to achieve the best results.
When testing the selected and scaled ground motions, it’s a standard procedure to use the nonlinear time history analysis to validate the results in terms of structural responses and their variation. this proves the efficiency of the presented procedure.
In this study, basic methodologies for selecting and scaling strong ground motion time histories are summarized, the selection and scaling criteria of real time history records to satisfy the Syrian design code are discussed. The GA scaling procedures are utilized to scale 10 set of records, every set consists of seven records of available real records to match the Syrian design spectra. The resulting time histories are investigated and compared in terms of suitability as input to time history analysis of civil engineering structures, by mean of time history analyses of SDOF systems which are conducted to examine the efficiency of the scaling method in reducing the scatter in structural response. The nonlinear response of SDOF systems is represented by bilinear hysteretic model. Assuming 5 different Periods, yield strength reduction factor, R= 4.5, α=3% post-yield stiffness, a number of 700 runs of analysis are conducted. The results are described for elastic displacement D.
Cold formed steel (CFS) has many advantages over other construction materials.
CFS members are lightweight. They weigh up to 30-35% less than their wood
counterparts.. This makes CFS members economical and the same time very easy to erect
and inst
all. They may be shaped (cold-bent) to nearly any open cross section. This allows
for the use of optimization technique’s to find optimal shapes for the members’ cross
sections.
The research aims to show the genetic algorithm's ability in determining the optimum
dimensions cold formed C section. To do so, the optimum design mathematical
formulation was formulated by adding the manufacturing constraints that reflect the
section folding operations in addition the geometrical and structural constraints.
The research found that the genetic algorithm is effective tool in finding the best
solution to this issue, as it showed its ability to deal with asymmetric section through
reaching solutions conform to the basic principles of mechanics of material.
The algorithm is adjustable, so that it can implement the design restrictions which are
compatible with any codes or any manufacturing requirements imposed by modulation
techniques.
Photovoltaic systems (PVs)offer an environmentally friendlysource of electricity;
however, up till now its price is still relatively high.Achieving the maximum power of
these systemsand maintaining it with lowest price in real applications is highl
y associated
with Maximum Power Point Tracking (MPPT) under different operation conditions.
This paper proposes the use of Genetic Algorithm (GA) for tracking maximum
power point depending on the solar cell model. GA gives, directly and precisely, the
optimal operating voltage (VOP) of the cell where the DC/DC converter will be adjusted
according to it based on the previous knowledge of the open circuit voltage (VOC) and
short circuit current (ISC) of the cell.
To validate the correctness and effectiveness of the proposed algorithm, MATLAB
R2010a programs for GA and PV system are written and incorporated together where the
series resistant of the cell is considered while the shunt resistant is neglected.
Simulation results of applying GA on different types of solar panels showedthe
possibilityof the accurateadjusting of the voltagetothe optimum valueand thusoperating the
systemat maximum power point.
In this study, basic methodologies of the GA and the scaling
procedures are summarized, the scaling criteria of real time history
records to satisfy the Syrian design code are discussed. The
traditional time domain scaling procedures and the scali
ng
procedures using GA are utilized to scale a number of the available
real records to match the Syrian design spectra. The resulting time histories of the procedures are investigated and compared in terms of meeting criteria.
