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: 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: 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: Fuzzy systems have been successfully used for
exchange rate forecasting. However, fuzzy system is very confusing
and complex to be designed by an expert, as there is a large set of
parameters (fuzzy knowledge base) that must be selected, it is not a
simple task to select the appropriate fuzzy knowledge base for an
exchange rate forecasting. The researchers often look the effect of
fuzzy knowledge base on the performances of fuzzy system
forecasting. This paper proposes a genetic fuzzy predictor to forecast
the future value of daily US Dollar/Euro exchange rate time’s series.
A range of methodologies based on a set of fuzzy predictor’s which
allow the forecasting of the same time series, but with a different
fuzzy partition. Each fuzzy predictor is built from two stages, where
each stage is performed by a real genetic algorithm.
Abstract: Economic Dispatch (ED) is one of the most
challenging problems of power system since it is difficult to determine
the optimum generation scheduling to meet the particular load demand
with the minimum fuel costs while all constraints are satisfied. The
objective of the Economic Dispatch Problems (EDPs) of electric
power generation is to schedule the committed generating units
outputs so as to meet the required load demand at minimum operating
cost while satisfying all units and system equality and inequality
constraints. In this paper, an efficient and practical steady-state genetic
algorithm (SSGAs) has been proposed for solving the economic
dispatch problem. The objective is to minimize the total generation
fuel cost and keep the power flows within the security limits. To
achieve that, the present work is developed to determine the optimal
location and size of capacitors in transmission power system where,
the Participation Factor Algorithm and the Steady State Genetic
Algorithm are proposed to select the best locations for the capacitors
and determine the optimal size for them.
Abstract: In this paper a nonlinear feedback control called augmented automatic choosing control (AACC) for a class of
nonlinear systems with constrained input is presented. When designed
the control, a constant term which arises from linearization of a
given nonlinear system is treated as a coefficient of a stable zero
dynamics. Parameters of the control are suboptimally selected by
maximizing the stable region in the sense of Lyapunov with the aid
of a genetic algorithm. This approach is applied to a field excitation
control problem of power system to demonstrate the splendidness
of the AACC. Simulation results show that the new controller can
improve performance remarkably well.
Abstract: This paper describes the design optimization of ferrocement-laminated plate made up of reinforcing steel wire mesh(es) and cement mortar. For the improvement of the designing process, the plate is modeled as a multi-layer medium, dividing the ferrocement plate into layers of mortar and ferrocement. The mortar layers are assumed to be isotropic in nature and the ferrocement layers are assumed to be orthotropic. The ferrocement layers are little stiffer, but much more costlier, than the mortar layers due the presence of steel wire mesh. The optimization is performed for minimum weight design of the laminate using a genetic algorithm. The optimum designs are discussed for different plate configurations and loadings, and it is compared with the worst designs obtained at the final generation. The paper provides a procedure for the designers in decision-making process.
Abstract: There are many difficulties in the purification of raw components and products. However, researchers are seeking better ways for purification. One of the recent methods is extraction using supercritical fluids. In this study, the phase equilibria of benzoic acid -supercritical carbon dioxide system were investigated. Regarding the phase equilibria of this system, the modeling of solid-supercritical fluid behavior was performed using the Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) and Peng-Robinson equations of state (PR EoS). For this purpose, five PC-SAFT EoS parameters for pure benzoic acid were obtained using its experimental vapor pressure. Benzoic acid has association sites and the behavior of the benzoic acid-supercritical fluid system was well predicted using both equations of state, while the binary interaction parameter values for PR EoS were negative. Genetic algorithm, which is one of the most accurate global optimization algorithms, was also used to optimize the pure benzoic acid parameters and the binary interaction parameters. The AAD% value for the PC-SAFT EoS, were 0.22 for the carbon dioxide-benzoic acid system.
Abstract: This paper deals with modeling and optimization of two NP-hard problems in production planning of flexible manufacturing system (FMS), part type selection problem and loading problem. The part type selection problem and the loading problem are strongly related and heavily influence the system’s efficiency and productivity. These problems have been modeled and solved simultaneously by using real coded genetic algorithms (RCGA) which uses an array of real numbers as chromosome representation. The novel proposed chromosome representation produces only feasible solutions which minimize a computational time needed by GA to push its population toward feasible search space or repair infeasible chromosomes. The proposed RCGA improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solutions in a reasonable amount of time.
Abstract: Cubic equations of state like Redlich–Kwong (RK)
EOS have been proved to be very reliable tools in the prediction of
phase behavior. Despite their good performance in compositional
calculations, they usually suffer from weaknesses in the predictions
of saturated liquid density. In this research, RK equation was
modified. The result of this study show that modified equation has
good agreement with experimental data.
Abstract: The Choquet integral is a tool for the information fusion that is very effective in the case where fuzzy measures associated with it are well chosen. In this paper, we propose a new approach for calculating fuzzy measures associated with the Choquet integral in a context of data fusion in multimodal biometrics. The proposed approach is based on genetic algorithms. It has been validated in two databases: the first base is relative to synthetic scores and the second one is biometrically relating to the face, fingerprint and palmprint. The results achieved attest the robustness of the proposed approach.
Abstract: The stiffness of the workpiece is very important to reduce the errors in manufacturing process. The high stiffness of the workpiece can be achieved by optimal positioning of fixture elements in the fixture. The minimization of the sum of the nodal deflection normal to the surface is used as objective function in previous research. The deflection in other direction has been neglected. The 3-2-1 fixturing principle is not valid for metal sheets due to its flexible nature. We propose a new fixture layout optimization method N-3-2-1 for metal sheets that uses the strain energy of the finite elements. This method combines the genetic algorithm and finite element analysis. The objective function in this method is to minimize the sum of all the element strain energy. By using the concept of element strain energy, the deformations in all the directions have been considered. Strain energy and stiffness are inversely proportional to each other. So, lower the value of strain energy, higher will be the stiffness. Two different kinds of case studies are presented. The case studies are solved for both objective functions; element strain energy and nodal deflection. The result are compared to verify the propose method.
Abstract: Discrete search path planning in time-constrained uncertain environment relying upon imperfect sensors is known to be hard, and current problem-solving techniques proposed so far to compute near real-time efficient path plans are mainly bounded to provide a few move solutions. A new information-theoretic –based open-loop decision model explicitly incorporating false alarm sensor readings, to solve a single agent military logistics search-and-delivery path planning problem with anticipated feedback is presented. The decision model consists in minimizing expected entropy considering anticipated possible observation outcomes over a given time horizon. The model captures uncertainty associated with observation events for all possible scenarios. Entropy represents a measure of uncertainty about the searched target location. Feedback information resulting from possible sensor observations outcomes along the projected path plan is exploited to update anticipated unit target occupancy beliefs. For the first time, a compact belief update formulation is generalized to explicitly include false positive observation events that may occur during plan execution. A novel genetic algorithm is then proposed to efficiently solve search path planning, providing near-optimal solutions for practical realistic problem instances. Given the run-time performance of the algorithm, natural extension to a closed-loop environment to progressively integrate real visit outcomes on a rolling time horizon can be easily envisioned. Computational results show the value of the approach in comparison to alternate heuristics.
Abstract: Short-Term Load Forecasting (STLF) plays an important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural networks for load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. We find good performance for the large neural networks. The proposed methodology gives lower percent errors all the time. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: In this paper we consider a nonlinear feedback control called augmented automatic choosing control (AACC) for nonlinear systems with constrained input. Constant terms which arise from section wise linearization of a given nonlinear system are treated as coefficients of a stable zero dynamics.Parameters included in the control are suboptimally selectedby extremizing a combination of Hamiltonian and Lyapunov functions with the aid of the genetic algorithm. This approach is applied to a field excitation control problem of power system to demonstrate the splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well.
Abstract: This paper summarizes and compares approaches to
solving the knapsack problem and its known application in capital
budgeting. The first approach uses deterministic methods and can be
applied to small-size tasks with a single constraint. We can also
apply commercial software systems such as the GAMS modelling
system. However, because of NP-completeness of the problem, more
complex problem instances must be solved by means of heuristic
techniques to achieve an approximation of the exact solution in a
reasonable amount of time. We show the problem representation and
parameter settings for a genetic algorithm framework.
Abstract: This paper presents an efficient approach to feeder
reconfiguration for power loss reduction and voltage profile
imprvement in unbalanced radial distribution systems (URDS). In
this paper Genetic Algorithm (GA) is used to obtain solution for
reconfiguration of radial distribution systems to minimize the losses.
A forward and backward algorithm is used to calculate load flows in
unbalanced distribution systems. By simulating the survival of the
fittest among the strings, the optimum string is searched by
randomized information exchange between strings by performing
crossover and mutation. Results have shown that proposed algorithm
has advantages over previous algorithms The proposed method is
effectively tested on 19 node and 25 node unbalanced radial
distribution systems.
Abstract: This paper investigates the problem of spreading
sequence and receiver code synchronization techniques for satellite
based CDMA communications systems. The performance of CDMA
system depends on the autocorrelation and cross-correlation
properties of the used spreading sequences. In this paper we propose
the uses of chaotic Lu system to generate binary sequences for
spreading codes in a direct sequence spread CDMA system. To
minimize multiple access interference (MAI) we propose the use of
genetic algorithm for optimum selection of chaotic spreading
sequences. To solve the problem of transmitter-receiver
synchronization, we use the passivity controls. The concept of
semipassivity is defined to find simple conditions which ensure
boundedness of the solutions of coupled Lu systems. Numerical
results are presented to show the effectiveness of the proposed
approach.
Abstract: Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to analog circuit design automation. These researches show a better performance due to the nature of Genetic Algorithm. In this paper a modified Genetic Algorithm is applied for analog circuit design automation. The modifications are made to the topology of the circuit. These modifications will lead to a more computationally efficient algorithm.
Abstract: Unmanned aerial vehicles (UAVs) performing their
operations for a long time have been attracting much attention in
military and civil aviation industries for the past decade. The
applicable field of UAV is changing from the military purpose only to
the civil one. Because of their low operation cost, high reliability and
the necessity of various application areas, numerous development
programs have been initiated around the world. To obtain the optimal
solutions of the design variable (i.e., sectional airfoil profile, wing
taper ratio and sweep) for high performance of UAVs, both the lift and
lift-to-drag ratio are maximized whereas the pitching moment should
be minimized, simultaneously. It is found that the lift force and
lift-to-drag ratio are linearly dependent and a unique and dominant
solution are existed. However, a trade-off phenomenon is observed
between the lift-to-drag ratio and pitching moment. As the result of
optimization, sixty-five (65) non-dominated Pareto individuals at the
cutting edge of design spaces that are decided by airfoil shapes can be
obtained.