Abstract: In this paper, a simple heuristic genetic algorithm is
used for Multistage Multiuser detection in fast fading environments.
Multipath channels, multiple access interference (MAI) and near far
effect cause the performance of the conventional detector to degrade.
Heuristic Genetic algorithms, a rapidly growing area of artificial
intelligence, uses evolutionary programming for initial search, which
not only helps to converge the solution towards near optimal
performance efficiently but also at a very low complexity as
compared with optimal detector. This holds true for Additive White
Gaussian Noise (AWGN) and multipath fading channels.
Experimental results are presented to show the superior performance
of the proposed techque over the existing methods.
Abstract: Complex assemblies of interacting proteins carry out
most of the interesting jobs in a cell, such as metabolism, DNA
synthesis, mitosis and cell division. These physiological properties
play out as a subtle molecular dance, choreographed by underlying
regulatory networks that control the activities of cyclin-dependent
kinases (CDK). The network can be modeled by a set of nonlinear
differential equations and its behavior predicted by numerical
simulation. In this paper, an innovative approach has been proposed
that uses genetic algorithms to mine a set of behavior data output by
a biological system in order to determine the kinetic parameters of
the system. In our approach, the machine learning method is
integrated with the framework of existent biological information in a
wiring diagram so that its findings are expressed in a form of system
dynamic behavior. By numerical simulations it has been illustrated
that the model is consistent with experiments and successfully shown
that such application of genetic algorithms will highly improve the
performance of mathematical model of the cell division cycle to
simulate such a complicated bio-system.
Abstract: Selecting the routes and the assignment of link flow in a computer communication networks are extremely complex combinatorial optimization problems. Metaheuristics, such as genetic or simulated annealing algorithms, are widely applicable heuristic optimization strategies that have shown encouraging results for a large number of difficult combinatorial optimization problems. This paper considers the route selection and hence the flow assignment problem. A genetic algorithm and simulated annealing algorithm are used to solve this problem. A new hybrid algorithm combining the genetic with the simulated annealing algorithm is introduced. A modification of the genetic algorithm is also introduced. Computational experiments with sample networks are reported. The results show that the proposed modified genetic algorithm is efficient in finding good solutions of the flow assignment problem compared with other techniques.
Abstract: Phylogenies ; The evolutionary histories of groups of
species are one of the most widely used tools throughout the life
sciences, as well as objects of research with in systematic,
evolutionary biology. In every phylogenetic analysis reconstruction
produces trees. These trees represent the evolutionary histories of
many groups of organisms, bacteria due to horizontal gene transfer
and plants due to process of hybridization. The process of gene
transfer in bacteria and hybridization in plants lead to reticulate
networks, therefore, the methods of constructing trees fail in
constructing reticulate networks. In this paper a model has been
employed to reconstruct phylogenetic network in honey bee. This
network represents reticulate evolution in honey bee. The maximum
parsimony approach has been used to obtain this reticulate network.
Abstract: Most of the losses in a power system relate to
the distribution sector which always has been considered.
From the important factors which contribute to increase losses
in the distribution system is the existence of radioactive flows.
The most common way to compensate the radioactive power
in the system is the power to use parallel capacitors. In
addition to reducing the losses, the advantages of capacitor
placement are the reduction of the losses in the release peak of
network capacity and improving the voltage profile. The point
which should be considered in capacitor placement is the
optimal placement and specification of the amount of the
capacitor in order to maximize the advantages of capacitor
placement.
In this paper, a new technique has been offered for the
placement and the specification of the amount of the constant
capacitors in the radius distribution network on the basis of
Genetic Algorithm (GA). The existing optimal methods for
capacitor placement are mostly including those which reduce
the losses and voltage profile simultaneously. But the
retaliation cost and load changes have not been considered as
influential UN the target function .In this article, a holistic
approach has been considered for the optimal response to this
problem which includes all the parameters in the distribution
network: The price of the phase voltage and load changes. So,
a vast inquiry is required for all the possible responses. So, in
this article, we use Genetic Algorithm (GA) as the most
powerful method for optimal inquiry.
Abstract: Optimization of rational geometrical and mechanical
parameters of panel with curved plywood ribs is considered in this
paper. The panel consists of cylindrical plywood ribs manufactured
from Finish plywood, upper and bottom plywood flange, stiffness
diaphragms. Panel is filled with foam. Minimal ratio of structure self
weight and load that could be applied to structure is considered as
rationality criteria. Optimization is done, by using classical beam
theory without nonlinearities. Optimization of discreet design
variables is done by Genetic algorithm.
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: Tumor classification is a key area of research in the
field of bioinformatics. Microarray technology is commonly used in
the study of disease diagnosis using gene expression levels. The
main drawback of gene expression data is that it contains thousands
of genes and a very few samples. Feature selection methods are used
to select the informative genes from the microarray. These methods
considerably improve the classification accuracy. In the proposed
method, Genetic Algorithm (GA) is used for effective feature
selection. Informative genes are identified based on the T-Statistics,
Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate
solutions of GA are obtained from top-m informative genes. The
classification accuracy of k-Nearest Neighbor (kNN) method is used
as the fitness function for GA. In this work, kNN and Support Vector
Machine (SVM) are used as the classifiers. The experimental results
show that the proposed work is suitable for effective feature
selection. With the help of the selected genes, GA-kNN method
achieves 100% accuracy in 4 datasets and GA-SVM method
achieves in 5 out of 10 datasets. The GA with kNN and SVM
methods are demonstrated to be an accurate method for microarray
based tumor classification.
Abstract: Shape optimization of the airfoil with high aspect ratio
of long endurance unmanned aerial vehicle (UAV) is performed by the
multi-objective optimization technology coupled with computational
fluid dynamics (CFD). For predicting the aerodynamic characteristics
around the airfoil the high-fidelity Navier-Stokes solver is employed
and SMOGA (Simple Multi-Objective Genetic Algorithm), which is
developed by authors, is used for solving the multi-objective
optimization problem. 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 is decided by airfoil shapes can be obtained.
Abstract: Restructured electricity markets may provide
opportunities for producers to exercise market power maintaining
prices in excess of competitive levels. In this paper an oligopolistic
market is presented that all Generation Companies (GenCos) bid in a
Cournot model. Genetic algorithm (GA) is applied to obtain
generation scheduling of each GenCo as well as hourly market
clearing prices (MCP). In order to consider network constraints a
multiperiod framework is presented to simulate market clearing
mechanism in which the behaviors of market participants are
modelled through piecewise block curves. A mixed integer linear
programming (MILP) is employed to solve the problem. Impacts of
market clearing process on participants- characteristic and final
market prices are presented. Consequently, a novel multi-objective
model is addressed for security constrained optimal bidding strategy
of GenCos. The capability of price-maker GenCos to alter MCP is
evaluated through introducing an effective-supply curve. In addition,
the impact of exercising market power on the variation of market
characteristics as well as GenCos scheduling is studied.
Abstract: In this paper, a novel method using Bees Algorithm is proposed to determine the optimal allocation of FACTS devices for maximizing the Available Transfer Capability (ATC) of power transactions between source and sink areas in the deregulated power system. The algorithm simultaneously searches the FACTS location, FACTS parameters and FACTS types. Two types of FACTS are simulated in this study namely Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC). A Repeated Power Flow with FACTS devices including ATC is used to evaluate the feasible ATC value within real and reactive power generation limits, line thermal limits, voltage limits and FACTS operation limits. An IEEE30 bus system is used to demonstrate the effectiveness of the algorithm as an optimization tool to enhance ATC. A Genetic Algorithm technique is used for validation purposes. The results clearly indicate that the introduction of FACTS devices in a right combination of location and parameters could enhance ATC and Bees Algorithm can be efficiently used for this kind of nonlinear integer optimization.
Abstract: Routing in MANET is extremely challenging because
of MANETs dynamic features, its limited bandwidth, frequent
topology changes caused by node mobility and power energy
consumption. In order to efficiently transmit data to destinations, the
applicable routing algorithms must be implemented in mobile ad-hoc
networks. Thus we can increase the efficiency of the routing by
satisfying the Quality of Service (QoS) parameters by developing
routing algorithms for MANETs. The algorithms that are inspired by
the principles of natural biological evolution and distributed
collective behavior of social colonies have shown excellence in
dealing with complex optimization problems and are becoming more
popular. This paper presents a survey on few meta-heuristic
algorithms and naturally-inspired algorithms.
Abstract: This paper presents a new method which applies an
artificial bee colony algorithm (ABC) for capacitor placement in
distribution systems with an objective of improving the voltage profile
and reduction of power loss. The ABC algorithm is a new population
based meta heuristic approach inspired by intelligent foraging behavior
of honeybee swarm. The advantage of ABC algorithm is that
it does not require external parameters such as cross over rate and
mutation rate as in case of genetic algorithm and differential evolution
and it is hard to determine these parameters in prior. The other
advantage is that the global search ability in the algorithm is implemented
by introducing neighborhood source production mechanism
which is a similar to mutation process. To demonstrate the validity
of the proposed algorithm, computer simulations are carried out on
69-bus system and compared the results with the other approach
available in the literature. The proposed method has outperformed the
other methods in terms of the quality of solution and computational
efficiency.
Abstract: This research elaborates decision models for product
innovation in the early phases, focusing on one of the most widely
implemented method in marketing research: conjoint analysis and the
related conjoint-based models with special focus on heuristics
programming techniques for the development of optimal product
innovation. The concept, potential, requirements and limitations of
conjoint analysis and its conjoint-based heuristics successors are
analysed and the development of conceptual framework of Genetic
Algorithm (GA) as one of the most widely implemented heuristic
methods for developing product innovations are discussed.
Abstract: This paper presents a systematic approach for designing Unified Power Flow Controller (UPFC) based supplementary damping controllers for damping low frequency oscillations in a single-machine infinite-bus power system. Detailed investigations have been carried out considering the four alternatives UPFC based damping controller namely modulating index of series inverter (mB), modulating index of shunt inverter (mE), phase angle of series inverter (δB ) and phase angle of the shunt inverter (δE ). The design problem of the proposed controllers is formulated as an optimization problem and Real- Coded Genetic Algorithm (RCGA) is employed to optimize damping controller parameters. Simulation results are presented and compared with a conventional method of tuning the damping controller parameters to show the effectiveness and robustness of the proposed design approach.
Abstract: The development of Artificial Neural Networks
(ANNs) is usually a slow process in which the human expert has to
test several architectures until he finds the one that achieves best
results to solve a certain problem. This work presents a new
technique that uses Genetic Programming (GP) for automatically
generating ANNs. To do this, the GP algorithm had to be changed in
order to work with graph structures, so ANNs can be developed. This
technique also allows the obtaining of simplified networks that solve
the problem with a small group of neurons. In order to measure the
performance of the system and to compare the results with other
ANN development methods by means of Evolutionary Computation
(EC) techniques, several tests were performed with problems based
on some of the most used test databases. The results of those
comparisons show that the system achieves good results comparable
with the already existing techniques and, in most of the cases, they
worked better than those techniques.
Abstract: This paper presents an approach for the design of
fuzzy logic power system stabilizers using genetic algorithms. In the
proposed fuzzy expert system, speed deviation and its derivative
have been selected as fuzzy inputs. In this approach the parameters of
the fuzzy logic controllers have been tuned using genetic algorithm.
Incorporation of GA in the design of fuzzy logic power system
stabilizer will add an intelligent dimension to the stabilizer and
significantly reduces computational time in the design process. It is
shown in this paper that the system dynamic performance can be
improved significantly by incorporating a genetic-based searching
mechanism. To demonstrate the robustness of the genetic based
fuzzy logic power system stabilizer (GFLPSS), simulation studies on
multimachine system subjected to small perturbation and three-phase
fault have been carried out. Simulation results show the superiority
and robustness of GA based power system stabilizer as compare to
conventionally tuned controller to enhance system dynamic
performance over a wide range of operating conditions.
Abstract: This paper presents a procedure for modeling and tuning the parameters of Thyristor Controlled Series Compensation (TCSC) controller in a multi-machine power system to improve transient stability. First a simple transfer function model of TCSC controller for stability improvement is developed and the parameters of the proposed controller are optimally tuned. Genetic algorithm (GA) is employed for the optimization of the parameter-constrained nonlinear optimization problem implemented in a simulation environment. By minimizing an objective function in which the oscillatory rotor angle deviations of the generators are involved, transient stability performance of the system is improved. The proposed TCSC controller is tested on a multi-machine system and the simulation results are presented. The nonlinear simulation results validate the effectiveness of proposed approach for transient stability improvement in a multimachine power system installed with a TCSC. The simulation results also show that the proposed TCSC controller is also effective in damping low frequency oscillations.
Abstract: Graph partitioning is a NP-hard problem with multiple
conflicting objectives. The graph partitioning should minimize the
inter-partition relationship while maximizing the intra-partition
relationship. Furthermore, the partition load should be evenly
distributed over the respective partitions. Therefore this is a multiobjective
optimization problem (MOO). One of the approaches to
MOO is Pareto optimization which has been used in this paper. The
proposed methods of this paper used to improve the performance are
injecting best solutions of previous runs into the first generation of
next runs and also storing the non-dominated set of previous
generations to combine with later generation's non-dominated set.
These improvements prevent the GA from getting stuck in the local
optima and increase the probability of finding more optimal
solutions. Finally, a simulation research is carried out to investigate
the effectiveness of the proposed algorithm. The simulation results
confirm the effectiveness of the proposed method.
Abstract: Brassinosteroids (BRs) regulate cell elongation,
vascular differentiation, senescence, and stress responses. BRs signal
through the BES1/BZR1 family of transcription factors, which
regulate hundreds of target genes involved in this pathway. In this
research a comprehensive genome-wide analysis was carried out in
BES1/BZR1 gene family in Arabidopsis thaliana, Cucumis sativus,
Vitis vinifera, Glycin max and Brachypodium distachyon.
Specifications of the desired sequences, dot plot and hydropathy plot
were analyzed in the protein and genome sequences of five plant
species. The maximum amino acid length was attributed to protein
sequence Brdic3g with 374aa and the minimum amino acid length
was attributed to protein sequence Gm7g with 163aa. The maximum
Instability index was attributed to protein sequence AT1G19350
equal with 79.99 and the minimum Instability index was attributed to
protein sequence Gm5g equal with 33.22. Aliphatic index of these
protein sequences ranged from 47.82 to 78.79 in Arabidopsis
thaliana, 49.91 to 57.50 in Vitis vinifera, 55.09 to 82.43 in Glycin
max, 54.09 to 54.28 in Brachypodium distachyon 55.36 to 56.83 in
Cucumis sativus. Overall, data obtained from our investigation
contributes a better understanding of the complexity of the
BES1/BZR1 gene family and provides the first step towards directing
future experimental designs to perform systematic analysis of the
functions of the BES1/BZR1 gene family.