Abstract: This study, for its research subjects, uses patients who
had undergone total knee replacement surgery from the database of the
National Health Insurance Administration. Through the review of
literatures and the interviews with physicians, important factors are
selected after careful screening. Then using Cross Entropy Method,
Genetic Algorithm Logistic Regression, and Particle Swarm
Optimization, the weight of each factor is calculated and obtained. In
the meantime, Excel VBA and Case Based Reasoning are combined
and adopted to evaluate the system. Results show no significant
difference found through Genetic Algorithm Logistic Regression and
Particle Swarm Optimization with over 97% accuracy in both
methods. Both ROC areas are above 0.87. This study can provide
critical reference to medical personnel as clinical assessment to
effectively enhance medical care quality and efficiency, prevent
unnecessary waste, and provide practical advantages to resource
allocation to medical institutes.
Abstract: This paper proposed the comparison made between
Multi-Carrier Pulse Width Modulation, Sinusoidal Pulse Width
Modulation and Selective Harmonic Elimination Pulse Width
Modulation technique for minimization of Total Harmonic Distortion
in Cascaded H-Bridge Multi-Level Inverter. In Multicarrier Pulse
Width Modulation method by using Alternate Position of Disposition
scheme for switching pulse generation to Multi-Level Inverter.
Another carrier based approach; Sinusoidal Pulse Width Modulation
method is also implemented to define the switching pulse generation
system in the multi-level inverter. In Selective Harmonic Elimination
method using Genetic Algorithm and Particle Swarm Optimization
algorithm for define the required switching angles to eliminate low
order harmonics from the inverter output voltage waveform and
reduce the total harmonic distortion value. So, the results validate that
the Selective Harmonic Elimination Pulse Width Modulation method
does capably eliminate a great number of precise harmonics and
minimize the Total Harmonic Distortion value in output voltage
waveform in compared with Multi-Carrier Pulse Width Modulation
method, Sinusoidal Pulse Width Modulation method. In this paper,
comparison of simulation results shows that the Selective Harmonic
Elimination method can attain optimal harmonic minimization
solution better than Multi-Carrier Pulse Width Modulation method,
Sinusoidal Pulse Width Modulation method.
Abstract: In this paper we propose a computer-aided solution
with Genetic Algorithms in order to reduce the drafting of reports:
FMEA analysis and Control Plan required in the manufacture of the
product launch and improved knowledge development teams for
future projects. The solution allows to the design team to introduce
data entry required to FMEA. The actual analysis is performed using
Genetic Algorithms to find optimum between RPN risk factor and
cost of production. A feature of Genetic Algorithms is that they are
used as a means of finding solutions for multi criteria optimization
problems. In our case, along with three specific FMEA risk factors is
considered and reduce production cost. Analysis tool will generate
final reports for all FMEA processes. The data obtained in FMEA
reports are automatically integrated with other entered parameters in
Control Plan. Implementation of the solution is in the form of an
application running in an intranet on two servers: one containing
analysis and plan generation engine and the other containing the
database where the initial parameters and results are stored. The
results can then be used as starting solutions in the synthesis of other
projects. The solution was applied to welding processes, laser cutting
and bending to manufacture chassis for buses. Advantages of the
solution are efficient elaboration of documents in the current project
by automatically generating reports FMEA and Control Plan using
multiple criteria optimization of production and build a solid
knowledge base for future projects. The solution which we propose is
a cheap alternative to other solutions on the market using Open
Source tools in implementation.
Abstract: The paper develops a Non-Linear Model Predictive
Control (NMPC) of water quality in Drinking Water Distribution
Systems (DWDS) based on the advanced non-linear quality dynamics
model including disinfections by-products (DBPs). A special attention
is paid to the analysis of an impact of the flow trajectories prescribed
by an upper control level of the recently developed two-time scale
architecture of an integrated quality and quantity control in DWDS.
The new quality controller is to operate within this architecture in the
fast time scale as the lower level quality controller. The controller
performance is validated by a comprehensive simulation study based
on an example case study DWDS.
Abstract: The Economic Lot Scheduling Problem (ELSP) is a
valuable mathematical model that can support decision-makers to
make scheduling decisions. The basic period approach is effective for
solving the ELSP. The assumption for applying the basic period
approach is that a product must use its maximum production rate to be
produced. However, a product can lower its production rate to reduce
the average total cost when a facility has extra idle time. The past
researches discussed how a product adjusts its production rate under
the common cycle approach. To the best of our knowledge, no studies
have addressed how a product lowers its production rate under the
basic period approach. This research is the first paper to discuss this
topic. The research develops a simple fixed rate approach that adjusts
the production rate of a product under the basic period approach to
solve the ELSP. Our numerical example shows our approach can find a
better solution than the traditional basic period approach. Our
mathematical model that applies the fixed rate approach under the
basic period approach can serve as a reference for other related
researches.
Abstract: In a practical power system, the power plants are not
located at the same distance from the center of loads and their fuel
costs are different. Also, under normal operating conditions, the
generation capacity is more than the total load demand and losses.
Thus, there are many options for scheduling generation. In an
interconnected power system, the objective is to find the real and
reactive power scheduling of each power plant in such a way as to
minimize the operating cost. This means that the generator’s real and
reactive powers are allowed to vary within certain limits so as to meet
a particular load demand with minimum fuel cost. This is called
optimal power flow problem. In this paper, Economic Load Dispatch
(ELD) of real power generation is considered. Economic Load
Dispatch (ELD) is the scheduling of generators to minimize total
operating cost of generator units subjected to equality constraint of
power balance within the minimum and maximum operating limits of
the generating units. In this paper, genetic algorithms are considered.
ELD solutions are found by solving the conventional load flow
equations while at the same time minimizing the fuel costs.
Abstract: Evolutionary optimization methods such as genetic
algorithms have been used extensively for the construction site layout
problem. More recently, ant colony optimization algorithms, which
are evolutionary methods based on the foraging behavior of ants,
have been successfully applied to benchmark combinatorial
optimization problems. This paper proposes a formulation of the site
layout problem in terms of a sequencing problem that is suitable for
solution using an ant colony optimization algorithm.
In the construction industry, site layout is a very important
planning problem. The objective of site layout is to position
temporary facilities both geographically and at the correct time such
that the construction work can be performed satisfactorily with
minimal costs and improved safety and working environment. During
the last decade, evolutionary methods such as genetic algorithms
have been used extensively for the construction site layout problem.
This paper proposes an ant colony optimization model for
construction site layout. A simple case study for a highway project is
utilized to illustrate the application of the model.
Abstract: The aim of this paper is to present the optimization
methodology developed in the frame of a Coastal Transport
Information System. The system will be used for the effective design
of coastal transportation lines and incorporates subsystems that
implement models, tools and techniques that may support the design
of improved networks. The role of the optimization and decision
subsystem is to provide the user with better and optimal scenarios
that will best fulfill any constrains, goals or requirements posed. The
complexity of the problem and the large number of parameters and
objectives involved led to the adoption of an evolutionary method
(Genetic Algorithms). The problem model and the subsystem
structure are presented in detail, and, its support for simulation is also
discussed.
Abstract: The key role in phenomenological modelling of cyclic
plasticity is good understanding of stress-strain behaviour of given
material. There are many models describing behaviour of materials
using numerous parameters and constants. Combination of individual
parameters in those material models significantly determines whether
observed and predicted results are in compliance. Parameter
identification techniques such as random gradient, genetic algorithm
and sensitivity analysis are used for identification of parameters using
numerical modelling and simulation. In this paper genetic algorithm
and sensitivity analysis are used to study effect of 4 parameters of
modified AbdelKarim-Ohno cyclic plasticity model. Results
predicted by Finite Element (FE) simulation are compared with
experimental data from biaxial ratcheting test with semi-elliptical
loading path.
Abstract: This work deals with parameter identification of
permanent magnet motors, a class of ac motor which is particularly
important in industrial automation due to characteristics like
applications high performance, are very attractive for applications
with limited space and reducing the need to eliminate because they
have reduced size and volume and can operate in a wide speed range,
without independent ventilation. By using experimental data and
genetic algorithm we have been able to extract values for both the
motor inductance and the electromechanical coupling constant, which
are then compared to measured and/or expected values.
Abstract: Power systems are operating under stressed condition
due to continuous increase in demand of load. This can lead to
voltage instability problem when face additional load increase or
contingency. In order to avoid voltage instability suitable size of
reactive power compensation at optimal location in the system is
required which improves the load margin. This work aims at
obtaining optimal size as well as location of compensation in the 39-
bus New England system with the help of Bacteria Foraging and
Genetic algorithms. To reduce the computational time the work
identifies weak candidate buses in the system, and then picks only
two of them to take part in the optimization. The objective function is
based on a recently proposed voltage stability index which takes into
account the weighted average sensitivity index is a simpler and faster
approach than the conventional CPF algorithm. BFOA has been
found to give better results compared to GA.
Abstract: The pursuit of the MPPT has led to the development of many kinds of controllers, one of which is the Fuzzy Logic controller, which has proven its worth. To further tune this controller this paper will discuss and analyze the use of Genetic Algorithms to tune the Fuzzy Logic Controller. It will provide an introduction to both systems, and test their compatibility and performance.
Abstract: Operations, maintenance and reliability of wind
turbines have received much attention over the years due to the rapid
expansion of wind farms. This paper explores early fault diagnosis
technique for a 5MW wind turbine system subjected to multiple
faults, where genetic optimization algorithm is employed to make the
residual sensitive to the faults, but robust against disturbances. The
proposed technique has a potential to reduce the downtime mostly
caused by the breakdown of components and exploit the productivity
consistency by providing timely fault alarms. Simulation results show
the effectiveness of the robust fault detection methods used under
Matlab/Simulink/Gatool environment.
Abstract: One image is worth more than thousand words.
Images if analyzed can reveal useful information. Low level image
processing deals with the extraction of specific feature from a single
image. Now the question arises: What technique should be used to
extract patterns of very large and detailed image database? The
answer of the question is: “Image Mining”. Image Mining deals with
the extraction of image data relationship, implicit knowledge, and
another pattern from the collection of images or image database. It is
nothing but the extension of Data Mining. In the following paper, not
only we are going to scrutinize the current techniques of image
mining but also present a new technique for mining images using
Genetic Algorithm.
Abstract: The emerging Cognitive Radio is combo of both the
technologies i.e. Radio dynamics and software technology. It involve
wireless system with efficient coding, designing, and making them
artificial intelligent to take the decision according to the surrounding
environment and adopt themselves accordingly, so as to deliver the
best QoS. This is the breakthrough from fixed hardware and fixed
utilization of the spectrum. This software-defined approach of
research is centralized at user-definition and application driven
model, various software method are used for the optimization of the
wireless communication. This paper focused on the Spectrum
allocation technique using genetic algorithm GA to evolve radio,
represented by chromosomes. The chromosomes gene represents the
adjustable parameters in given radio and by using GA, evolving over
the generations, the optimized set of parameters are evolved, as per
the requirement of user and availability of the spectrum, in our
prototype the gene consist of 6 different parameters, and the best set
of parameters are evolved according to the application need and
availability of the spectrum holes and thus maintaining best QoS for
user, simultaneously maintaining licensed user rights. The analyzing
tool Matlab is used for the performance of the prototype.
Abstract: This paper presents an evolutionary algorithm for
solving multi-objective optimization problems-based artificial neural
network (ANN). The multi-objective evolutionary algorithm used in
this study is genetic algorithm while ANN used is radial basis
function network (RBFN). The proposed algorithm named memetic
elitist Pareto non-dominated sorting genetic algorithm-based RBFN
(MEPGAN). The proposed algorithm is implemented on medical
diseases problems. The experimental results indicate that the
proposed algorithm is viable, and provides an effective means to
design multi-objective RBFNs with good generalization capability
and compact network structure. This study shows that MEPGAN
generates RBFNs coming with an appropriate balance between
accuracy and simplicity, comparing to the other algorithms found in
literature.
Abstract: In this paper a new algorithm to generate random
simple polygons from a given set of points in a two dimensional
plane is designed. The proposed algorithm uses a genetic algorithm to
generate polygons with few vertices. A new merge algorithm is
presented which converts any two polygons into a simple polygon.
This algorithm at first changes two polygons into a polygonal chain
and then the polygonal chain is converted into a simple polygon. The
process of converting a polygonal chain into a simple polygon is
based on the removal of intersecting edges. The experiments results
show that the proposed algorithm has the ability to generate a great
number of different simple polygons and has better performance in
comparison to celebrated algorithms such as space partitioning and
steady growth.
Abstract: This paper presents optimization of makespan for ‘n’
jobs and ‘m’ machines flexible job shop scheduling problem with
sequence dependent setup time using genetic algorithm (GA)
approach. A restart scheme has also been applied to prevent the
premature convergence. Two case studies are taken into
consideration. Results are obtained by considering crossover
probability (pc = 0.85) and mutation probability (pm = 0.15). Five
simulation runs for each case study are taken and minimum value
among them is taken as optimal makespan. Results indicate that
optimal makespan can be achieved with more than one sequence of
jobs in a production order.
Abstract: The ad hoc networks are the future of wireless
technology as everyone wants fast and accurate error free information
so keeping this in mind Bit Error Rate (BER) and power is optimized
in this research paper by using the Genetic Algorithm (GA). The
digital modulation techniques used for this paper are Binary Phase
Shift Keying (BPSK), M-ary Phase Shift Keying (M-ary PSK), and
Quadrature Amplitude Modulation (QAM). This work is
implemented on Wireless Ad Hoc Networks (WLAN). Then it is
analyze which modulation technique is performing well to optimize
the BER and power of WLAN.
Abstract: Today’s modern interconnected power system is
highly complex in nature. In this, one of the most important
requirements during the operation of the electric power system is the
reliability and security. Power and frequency oscillation damping
mechanism improve the reliability. Because of power system
stabilizer (PSS) low speed response against of major fault such as
three phase short circuit, FACTs devise that can control the network
condition in very fast time, are becoming popular. But FACTs
capability can be seen in a major fault present when nonlinear models
of FACTs devise and power system equipment are applied. To realize
this aim, the model of multi-machine power system with FACTs
controller is developed in MATLAB/SIMULINK using Sim Power
System (SPS) blockiest. Among the FACTs device, Static
synchronous series compensator (SSSC) due to high speed changes
its reactance characteristic inductive to capacitive, is effective power
flow controller. Tuning process of controller parameter can be
performed using different method. But Genetic Algorithm (GA)
ability tends to use it in controller parameter tuning process. In this
paper firstly POD controller is used to power oscillation damping.
But in this station, frequency oscillation dos not has proper damping
situation. So FOD controller that is tuned using GA is using that
cause to damp out frequency oscillation properly and power
oscillation damping has suitable situation.