Abstract: Fine-grained data replication over the Internet allows duplication of frequently accessed data objects, as opposed to entire sites, to certain locations so as to improve the performance of largescale content distribution systems. In a distributed system, agents representing their sites try to maximize their own benefit since they are driven by different goals such as to minimize their communication costs, latency, etc. In this paper, we will use game theoretical techniques and in particular auctions to identify a bidding mechanism that encapsulates the selfishness of the agents, while having a controlling hand over them. In essence, the proposed game theory based mechanism is the study of what happens when independent agents act selfishly and how to control them to maximize the overall performance. A bidding mechanism asks how one can design systems so that agents- selfish behavior results in the desired system-wide goals. Experimental results reveal that this mechanism provides excellent solution quality, while maintaining fast execution time. The comparisons are recorded against some well known techniques such as greedy, branch and bound, game theoretical auctions and genetic algorithms.
Abstract: Project managers are the ultimate responsible for the
overall characteristics of a project, i.e. they should deliver the project
on time with minimum cost and with maximum quality. It is vital for
any manager to decide a trade-off between these conflicting
objectives and they will be benefited of any scientific decision
support tool. Our work will try to determine optimal solutions (rather
than a single optimal solution) from which the project manager will
select his desirable choice to run the project. In this paper, the
problem in project scheduling notated as
(1,T|cpm,disc,mu|curve:quality,time,cost) will be studied. The
problem is multi-objective and the purpose is finding the Pareto
optimal front of time, cost and quality of a project
(curve:quality,time,cost), whose activities belong to a start to finish
activity relationship network (cpm) and they can be done in different
possible modes (mu) which are non-continuous or discrete (disc), and
each mode has a different cost, time and quality . The project is
constrained to a non-renewable resource i.e. money (1,T). Because
the problem is NP-Hard, to solve the problem, a meta-heuristic is
developed based on a version of genetic algorithm specially adapted
to solve multi-objective problems namely FastPGA. A sample project
with 30 activities is generated and then solved by the proposed
method.
Abstract: A computational platform is presented in this
contribution. It has been designed as a virtual laboratory to be used
for exploring optimization algorithms in biological problems. This
platform is built on a blackboard-based agent architecture. As a test
case, the version of the platform presented here is devoted to the
study of protein folding, initially with a bead-like description of the
chain and with the widely used model of hydrophobic and polar
residues (HP model). Some details of the platform design are
presented along with its capabilities and also are revised some
explorations of the protein folding problems with different types of
discrete space. It is also shown the capability of the platform to
incorporate specific tools for the structural analysis of the runs in
order to understand and improve the optimization process.
Accordingly, the results obtained demonstrate that the ensemble of
computational tools into a single platform is worthwhile by itself,
since experiments developed on it can be designed to fulfill different
levels of information in a self-consistent fashion. By now, it is being
explored how an experiment design can be useful to create a
computational agent to be included within the platform. These
inclusions of designed agents –or software pieces– are useful for the
better accomplishment of the tasks to be developed by the platform.
Clearly, while the number of agents increases the new version of the
virtual laboratory thus enhances in robustness and functionality.
Abstract: In this contribution, the use of a new genetic operator is proposed. The main advantage of using this operator is that it is able to assist the evolution procedure to converge faster towards the optimal solution of a problem. This new genetic operator is called ''intuition'' operator. Generally speaking, one can claim that this operator is a way to include any heuristic or any other local knowledge, concerning the problem, that cannot be embedded in the fitness function. Simulation results show that the use of this operator increases significantly the performance of the classic Genetic Algorithm by increasing the convergence speed of its population.
Abstract: Topology Optimization is a defined as the method of
determining optimal distribution of material for the assumed design
space with functionality, loads and boundary conditions [1].
Topology optimization can be used to optimize shape for the
purposes of weight reduction, minimizing material requirements or
selecting cost effective materials [2]. Topology optimization has been
implemented through the use of finite element methods for the
analysis, and optimization techniques based on the method of moving
asymptotes, genetic algorithms, optimality criteria method, level sets
and topological derivatives. Case study of Typical “Fuselage design"
is considered for this paper to explain the benefits of Topology
Optimization in the design cycle. A cylindrical shell is assumed as
the design space and aerospace standard pay loads were applied on
the fuselage with wing attachments as constraints. Then topological
optimization is done using Finite Element (FE) based software. This
optimization results in the structural concept design which satisfies
all the design constraints using minimum material.
Abstract: In this paper we present, propose and examine
additional membership functions for the Smoothing Transition
Autoregressive (STAR) models. More specifically, we present the
tangent hyperbolic, Gaussian and Generalized bell functions.
Because Smoothing Transition Autoregressive (STAR) models
follow fuzzy logic approach, more fuzzy membership functions
should be tested. Furthermore, fuzzy rules can be incorporated or
other training or computational methods can be applied as the error
backpropagation or genetic algorithm instead to nonlinear squares.
We examine two macroeconomic variables of US economy, the
inflation rate and the 6-monthly treasury bills interest rates.
Abstract: This paper proposes a new methodology for the
optimal allocation and sizing of Embedded Generation (EG)
employing Real Coded Genetic Algorithm (RCGA) to minimize the
total power losses and to improve voltage profiles in the radial
distribution networks. RCGA is a method that uses continuous
floating numbers as representation which is different from
conventional binary numbers. The RCGA is used as solution tool,
which can determine the optimal location and size of EG in radial
system simultaneously. This method is developed in MATLAB. The
effect of EG units- installation and their sizing to the distribution
networks are demonstrated using 24 bus system.
Abstract: This paper presents a systematic approach for the
design of power system stabilizer using genetic algorithm and
investigates the robustness of the GA based PSS. The proposed
approach employs GA search for optimal setting of PSS parameters.
The performance of the proposed GPSS under small and large
disturbances, loading conditions and system parameters is tested.
The eigenvalue analysis and nonlinear simulation results show the
effectiveness of the GPSS to damp out the system oscillations. It is
found tat the dynamic performance with the GPSS shows improved
results, over conventionally tuned PSS over a wide range of
operating conditions.
Abstract: In this paper, a pipelined version of genetic algorithm,
called PLGA, and a corresponding hardware platform are described.
The basic operations of conventional GA (CGA) are made pipelined
using an appropriate selection scheme. The selection operator, used
here, is stochastic in nature and is called SA-selection. This helps
maintaining the basic generational nature of the proposed pipelined
GA (PLGA). A number of benchmark problems are used to compare
the performances of conventional roulette-wheel selection and the
SA-selection. These include unimodal and multimodal functions with
dimensionality varying from very small to very large. It is seen that
the SA-selection scheme is giving comparable performances with
respect to the classical roulette-wheel selection scheme, for all the
instances, when quality of solutions and rate of convergence are considered.
The speedups obtained by PLGA for different benchmarks
are found to be significant. It is shown that a complete hardware
pipeline can be developed using the proposed scheme, if parallel
evaluation of the fitness expression is possible. In this connection
a low-cost but very fast hardware evaluation unit is described.
Results of simulation experiments show that in a pipelined hardware
environment, PLGA will be much faster than CGA. In terms of
efficiency, PLGA is found to outperform parallel GA (PGA) also.
Abstract: This paper presents a genetic algorithm based
approach for solving security constrained optimal power flow
problem (SCOPF) including FACTS devices. The optimal location of
FACTS devices are identified using an index called overload index
and the optimal values are obtained using an enhanced genetic
algorithm. The optimal allocation by the proposed method optimizes
the investment, taking into account its effects on security in terms of
the alleviation of line overloads. The proposed approach has been
tested on IEEE-30 bus system to show the effectiveness of the
proposed algorithm for solving the SCOPF problem.
Abstract: In this paper, we propose a morphing method by which face color images can be freely transformed. The main focus of this work is the transformation of one face image to another. This method is fully automatic in that it can morph two face images by automatically detecting all the control points necessary to perform the morph. A face detection neural network, edge detection and medium filters are employed to detect the face position and features. Five control points, for both the source and target images, are then extracted based on the facial features. Triangulation method is then used to match and warp the source image to the target image using the control points. Finally color interpolation is done using a color Gaussian model that calculates the color for each particular frame depending on the number of frames used. A real coded Genetic algorithm is used in both the image warping and color blending steps to assist in step size decisions and speed up the morphing. This method results in ''very smooth'' morphs and is fast to process.
Abstract: Set covering problem is a classical problem in
computer science and complexity theory. It has many applications,
such as airline crew scheduling problem, facilities location problem,
vehicle routing, assignment problem, etc. In this paper, three
different techniques are applied to solve set covering problem.
Firstly, a mathematical model of set covering problem is introduced
and solved by using optimization solver, LINGO. Secondly, the
Genetic Algorithm Toolbox available in MATLAB is used to solve
set covering problem. And lastly, an ant colony optimization method
is programmed in MATLAB programming language. Results
obtained from these methods are presented in tables. In order to
assess the performance of the techniques used in this project, the
benchmark problems available in open literature are used.
Abstract: In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Algorithm (GA) as an optimization technique with a direct use of real variables was employed. Yet, since the optimization via GAs is a long process, and the major time is consumed through the analysis, Radial Basis Function Neural Networks (RBFNN) was employed in predicting the output from the analysis. Thus, the process of optimization will be carried out through a hybrid neuro-GA environment, and the procedure will be carried out until a predicted optimum solution is achieved.
Abstract: This paper describes a practical approach to design
and develop a hybrid learning with acceleration feedback control
(HLC) scheme for input tracking and end-point vibration suppression
of flexible manipulator systems. Initially, a collocated proportionalderivative
(PD) control scheme using hub-angle and hub-velocity
feedback is developed for control of rigid-body motion of the system.
This is then extended to incorporate a further hybrid control scheme
of the collocated PD control and iterative learning control with
acceleration feedback using genetic algorithms (GAs) to optimize the
learning parameters. Experimental results of the response of the
manipulator with the control schemes are presented in the time and
frequency domains. The performance of the HLC is assessed in terms
of input tracking, level of vibration reduction at resonance modes and
robustness with various payloads.
Abstract: Travelling salesman problem (TSP) is a combinational
optimization problem and solution approaches have been applied
many real world problems. Pure TSP assumes the cities to visit are
fixed in time and thus solutions are created to find shortest path
according to these point. But some of the points are canceled to visit
in time. If the problem is not time crucial it is not important to
determine new routing plan but if the points are changing rapidly and
time is necessary do decide a new route plan a new approach should
be applied in such cases. We developed a route plan transfer method
based on transfer learning and we achieved high performance against
determining a new model from scratch in every change.
Abstract: A direct search approach to determine optimal reservoir operating is proposed with ant colony optimization for continuous domains (ACOR). The model is applied to a system of single reservoir to determine the optimum releases during 42 years of monthly steps. A disadvantage of ant colony based methods and the ACOR in particular, refers to great amount of computer run time consumption. In this study a highly effective procedure for decreasing run time has been developed. The results are compared to those of a GA based model.
Abstract: In this paper, we present a novel, principled approach to resolve the remained problems of substitution technique of audio steganography. Using the proposed genetic algorithm, message bits are embedded into multiple, vague and higher LSB layers, resulting in increased robustness. The robustness specially would be increased against those intentional attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well.
Abstract: Information hiding for authenticating and verifying the content integrity of the multimedia has been exploited extensively in the last decade. We propose the idea of using genetic algorithm and non-deterministic dependence by involving the un-watermarkable coefficients for digital image authentication. Genetic algorithm is used to intelligently select coefficients for watermarking in a DCT based image authentication scheme, which implicitly watermark all the un-watermarkable coefficients also, in order to thwart different attacks. Experimental results show that such intelligent selection results in improvement of imperceptibility of the watermarked image, and implicit watermarking of all the coefficients improves security against attacks such as cover-up, vector quantization and transplantation.
Abstract: Electronic Systems are the core of everyday lives.
They form an integral part in financial networks, mass transit,
telephone systems, power plants and personal computers. Electronic
systems are increasingly based on complex VLSI (Very Large Scale
Integration) integrated circuits. Initial electronic design automation is
concerned with the design and production of VLSI systems. The next
important step in creating a VLSI circuit is Physical Design. The
input to the physical design is a logical representation of the system
under design. The output of this step is the layout of a physical
package that optimally or near optimally realizes the logical
representation. Physical design problems are combinatorial in nature
and of large problem sizes. Darwin observed that, as variations are
introduced into a population with each new generation, the less-fit
individuals tend to extinct in the competition of basic necessities.
This survival of fittest principle leads to evolution in species. The
objective of the Genetic Algorithms (GA) is to find an optimal
solution to a problem .Since GA-s are heuristic procedures that can
function as optimizers, they are not guaranteed to find the optimum,
but are able to find acceptable solutions for a wide range of
problems. This survey paper aims at a study on Efficient Algorithms
for VLSI Physical design and observes the common traits of the
superior contributions.
Abstract: We consider a typical problem in the assembly of
printed circuit boards (PCBs) in a two-machine flow shop system to
simultaneously minimize the weighted sum of weighted tardiness and
weighted flow time. The investigated problem is a group scheduling
problem in which PCBs are assembled in groups and the interest is to
find the best sequence of groups as well as the boards within each
group to minimize the objective function value. The type of setup
operation between any two board groups is characterized as carryover
sequence-dependent setup time, which exactly matches with the real
application of this problem. As a technical constraint, all of the
boards must be kitted before the assembly operation starts (kitting
operation) and by kitting staff. The main idea developed in this paper
is to completely eliminate the role of kitting staff by assigning the
task of kitting to the machine operator during the time he is idle
which is referred to as integration of internal (machine) and external
(kitting) setup times. Performing the kitting operation, which is a
preparation process of the next set of boards while the other boards
are currently being assembled, results in the boards to continuously
enter the system or have dynamic arrival times. Consequently, a
dynamic PCB assembly system is introduced for the first time in the
assembly of PCBs, which also has characteristics similar to that of
just-in-time manufacturing. The problem investigated is
computationally very complex, meaning that finding the optimal
solutions especially when the problem size gets larger is impossible.
Thus, a heuristic based on Genetic Algorithm (GA) is employed. An
example problem on the application of the GA developed is
demonstrated and also numerical results of applying the GA on
solving several instances are provided.