Abstract: With the increasing complexity of engineering
problems, the traditional, single-objective and deterministic
optimization method can not meet people-s requirements. A
multi-objective fuzzy optimization model of resource input is built for
M chlor-alkali chemical eco-industrial park in this paper. First, the
model is changed into the form that can be solved by genetic algorithm
using fuzzy theory. And then, a fitness function is constructed for
genetic algorithm. Finally, a numerical example is presented to show
that the method compared with traditional single-objective
optimization method is more practical and efficient.
Abstract: A new stochastic algorithm called Probabilistic Global Search Johor (PGSJ) has recently been established for global optimization of nonconvex real valued problems on finite dimensional Euclidean space. In this paper we present convergence guarantee for this algorithm in probabilistic sense without imposing any more condition. Then, we jointly utilize this algorithm along with control
parameterization technique for the solution of constrained optimal control problem. The numerical simulations are also included to illustrate the efficiency and effectiveness of the PGSJ algorithm in the solution of control problems.
Abstract: The geometric errors in the manufacturing process can
be reduced by optimal positioning of the fixture elements in the
fixture to make the workpiece stiff. We propose a new fixture layout
optimization method N-3-2-1 for large metal sheets in this paper that
combines the genetic algorithm and finite element analysis. The
objective function in this method is to minimize the sum of the nodal
deflection normal to the surface of the workpiece. Two different
kinds of case studies are presented, and optimal position of the
fixturing element is obtained for different cases.
Abstract: This work presents a new algorithm based on a combination of fuzzy (FUZ), Dynamic Programming (DP), and Genetic Algorithm (GA) approach for capacitor allocation in distribution feeders. The problem formulation considers two distinct objectives related to total cost of power loss and total cost of capacitors including the purchase and installation costs. The novel formulation is a multi-objective and non-differentiable optimization problem. The proposed method of this article uses fuzzy reasoning for sitting of capacitors in radial distribution feeders, DP for sizing and finally GA for finding the optimum shape of membership functions which are used in fuzzy reasoning stage. The proposed method has been implemented in a software package and its effectiveness has been verified through a 9-bus radial distribution feeder for the sake of conclusions supports. A comparison has been done among the proposed method of this paper and similar methods in other research works that shows the effectiveness of the proposed method of this paper for solving optimum capacitor planning problem.
Abstract: We present a novel scheme to evaluate sinusoidal functions with low complexity and high precision using cubic spline interpolation. To this end, two different approaches are proposed to find the interpolating polynomial of sin(x) within the range [- π , π]. The first one deals with only a single data point while the other with two to keep the realization cost as low as possible. An approximation error optimization technique for cubic spline interpolation is introduced next and is shown to increase the interpolator accuracy without increasing complexity of the associated hardware. The architectures for the proposed approaches are also developed, which exhibit flexibility of implementation with low power requirement.
Abstract: Fluid flow and heat transfer of vertical full cone
embedded in porous media is studied in this paper. Nonlinear
differential equation arising from similarity solution of inverted cone
(subjected to wall temperature boundary conditions) embedded in
porous medium is solved using a hybrid neural network- particle
swarm optimization method.
To aim this purpose, a trial solution of the differential equation is
defined as sum of two parts. The first part satisfies the initial/
boundary conditions and does contain an adjustable parameter and
the second part which is constructed so as not to affect the
initial/boundary conditions and involves adjustable parameters (the
weights and biases) for a multi-layer perceptron neural network.
Particle swarm optimization (PSO) is applied to find adjustable
parameters of trial solution (in first and second part). The obtained
solution in comparison with the numerical ones represents a
remarkable accuracy.
Abstract: We study the spatial design of experiment and we want to select a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology. In these applications, observations can be collected at different locations and possibly at different times. In spatial design, when the design region and the set of interest are discrete then the covariance matrix completely describe any objective function and our goal is to choose a feasible design that minimizes the resulting uncertainty. The problem is recast as that of maximizing the determinant of the covariance matrix of the chosen subset. This problem is NP-hard. For using these designs in computer experiments, in many cases, the design space is very large and it's not possible to calculate the exact optimal solution. Heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective and exact solution not possible. We developed a GA algorithm to take advantage of the exploratory power of this algorithm. The successful application of this method is demonstrated in large design space. We consider a real case of design of experiment. In our problem, design space is very large and for solving the problem, we used proposed GA algorithm.
Abstract: We present a new method for the fully automatic 3D
reconstruction of the coronary artery centerlines, using two X-ray
angiogram projection images from a single rotating monoplane
acquisition system. During the first stage, the input images are
smoothed using curve evolution techniques. Next, a simple yet
efficient multiscale method, based on the information of the Hessian
matrix, for the enhancement of the vascular structure is introduced.
Hysteresis thresholding using different image quantiles, is used to
threshold the arteries. This stage is followed by a thinning procedure
to extract the centerlines. The resulting skeleton image is then pruned
using morphological and pattern recognition techniques to remove
non-vessel like structures. Finally, edge-based stereo correspondence
is solved using a parallel evolutionary optimization method based on
f symbiosis. The detected 2D centerlines combined with disparity
map information allow the reconstruction of the 3D vessel
centerlines. The proposed method has been evaluated on patient data
sets for evaluation purposes.
Abstract: In this paper we introduce an approach via optimization methods to find approximate solutions for nonlinear Fredholm integral equations of the first kind. To
this purpose, we consider two stages of approximation.
First we convert the integral equation to a moment problem and then we modify the new problem to two classes of optimization problems, non-constraint optimization problems
and optimal control problems. Finally numerical examples is
proposed.
Abstract: In recent years, environment regulation forcing
manufactures to consider recovery activity of end-of- life products
and/or return products for refurbishing, recycling,
remanufacturing/repair and disposal in supply chain management. In
this paper, a mathematical model is formulated for single product
production-inventory system considering remanufacturing/reuse of
return products and rate of return products follows a demand like
function, dependent on purchasing price and acceptance quality level.
It is useful in decision making to determine whether to go for
remanufacturing or disposal of returned products along with newly
produced products to satisfy a stationary demand. In addition, a
modified genetic algorithm approach is proposed, inspired by particle
swarm optimization method. Numerical analysis of the case study is
carried out to validate the model.
Abstract: The artificial intelligent controller in power system
plays as most important rule for many applications such as system
operation and its control specially Load Frequency Controller (LFC).
The main objective of LFC is to keep the frequency and tie-line power
close to their decidable bounds in case of disturbance. In this paper,
parallel fuzzy PI adaptive with conventional PD technique for Load
Frequency Control system was proposed. PSO optimization method
used to optimize both of scale fuzzy PI and tuning of PD. Two equal
interconnected power system areas were used as a test system.
Simulation results show the effectiveness of the proposed controller
compared with different PID and classical fuzzy PI controllers in terms
of speed response and damping frequency.
Abstract: Selective harmonic elimination-pulse width modulation techniques offer a tight control of the harmonic spectrum of a given voltage waveform generated by a power electronic converter along with a low number of switching transitions. Traditional optimization methods suffer from various drawbacks, such as prolonged and tedious computational steps and convergence to local optima; thus, the more the number of harmonics to be eliminated, the larger the computational complexity and time. This paper presents a novel method for output voltage harmonic elimination and voltage control of PWM AC/AC voltage converters using the principle of hybrid Real-Coded Genetic Algorithm-Pattern Search (RGA-PS) method. RGA is the primary optimizer exploiting its global search capabilities, PS is then employed to fine tune the best solution provided by RGA in each evolution. The proposed method enables linear control of the fundamental component of the output voltage and complete elimination of its harmonic contents up to a specified order. Theoretical studies have been carried out to show the effectiveness and robustness of the proposed method of selective harmonic elimination. Theoretical results are validated through simulation studies using PSIM software package.
Abstract: This paper considers the influence of promotion
instruments for renewable energy sources (RES) on a multi-energy
modeling framework. In Europe, so called Feed-in Tariffs are
successfully used as incentive structures to increase the amount of
energy produced by RES. Because of the stochastic nature of large
scale integration of distributed generation, many problems have
occurred regarding the quality and stability of supply. Hence, a
macroscopic model was developed in order to optimize the power
supply of the local energy infrastructure, which includes electricity,
natural gas, fuel oil and district heating as energy carriers. Unique
features of the model are the integration of RES and the adoption of
Feed-in Tariffs into one optimization stage. Sensitivity studies are
carried out to examine the system behavior under changing profits
for the feed-in of RES. With a setup of three energy exchanging
regions and a multi-period optimization, the impact of costs and
profits are determined.
Abstract: As the electrical power industry is restructured, the electrical power exchange is becoming extended. One of the key information used to determine how much power can be transferred through the network is known as available transfer capability (ATC). To calculate ATC, traditional deterministic approach is based on the severest case, but the approach has the complexity of procedure. Therefore, novel approach for ATC calculation is proposed using cost-optimization method in this paper, and is compared with well-being method and risk-benefit method. This paper proposes the optimal transfer capability of HVDC system between mainland and a separated island in Korea through these three methods. These methods will consider production cost, wheeling charge through HVDC system and outage cost with one depth (N-1 contingency)
Abstract: A design flow of multi-standard down-conversion
CMOS mixers for three modern standards: Global System Mobile,
Digital Enhanced Cordless Telephone and Universal Mobile
Telecommunication Systems is presented. Three active mixer-s
structures are studied. The first is based on the Gilbert cell which
gives a tolerable noise figure and linearity with a low conversion
gain. The second and third structures use the current bleeding and
charge injection techniques in order to increase the conversion gain.
An improvement of about 2 dB of the conversion gain is achieved
without a considerable degradation of the other characteristics. The
models used for noise figure, conversion gain and IIP3 used are
studied. This study describes the nature of trade-offs inherent in such
structures and gives insights that help in identifying which structure
is better for given conditions.
Abstract: Clustering is one of an interesting data mining topics
that can be applied in many fields. Recently, the problem of cluster
analysis is formulated as a problem of nonsmooth, nonconvex optimization,
and an algorithm for solving the cluster analysis problem
based on nonsmooth optimization techniques is developed. This
optimization problem has a number of characteristics that make it
challenging: it has many local minimum, the optimization variables
can be either continuous or categorical, and there are no exact
analytical derivatives. In this study we show how to apply a particular
class of optimization methods known as pattern search methods
to address these challenges. These methods do not explicitly use
derivatives, an important feature that has not been addressed in
previous studies. Results of numerical experiments are presented
which demonstrate the effectiveness of the proposed method.