Abstract: In the real application of active control systems to
mitigate the response of structures subjected to sever external
excitations such as earthquake and wind induced vibrations, since the
capacity of actuators is limited then the actuators saturate. Hence, in
designing controllers for linear and nonlinear structures under sever
earthquakes, the actuator saturation should be considered as a
constraint. In this paper optimal design of active controllers for
nonlinear structures by considering the actuator saturation has been
studied. To this end a method has been proposed based on defining
an optimization problem which considers the minimizing of the
maximum displacement of the structure as objective when a limited
capacity for actuator has been used as a constraint in optimization
problem. To evaluate the effectiveness of the proposed method, a
single degree of freedom (SDF) structure with a bilinear hysteretic
behavior has been simulated under a white noise ground acceleration
of different amplitudes. Active tendon control mechanism, comprised
of pre-stressed tendons and an actuator, and extended nonlinear
Newmark method based instantaneous optimal control algorithm
have been used as active control mechanism and algorithm. To
enhance the efficiency of the controllers, the weights corresponding
to displacement, velocity, acceleration and control force in the
performance index have been found by using the Distributed Genetic
Algorithm (DGA). According to the results it has been concluded
that the proposed method has been effective in considering the
actuator saturation in designing optimal controllers for nonlinear
frames. Also it has been shown that the actuator capacity and the
average value of required control force are two important factors in
designing nonlinear controllers for considering the actuator
saturation.
Abstract: Due to the non- intuitive nature of Quantum
algorithms, it becomes difficult for a classically trained person to
efficiently construct new ones. So rather than designing new
algorithms manually, lately, Genetic algorithms (GA) are being
implemented for this purpose. GA is a technique to automatically
solve a problem using principles of Darwinian evolution. This has
been implemented to explore the possibility of evolving an n-qubit
circuit when the circuit matrix has been provided using a set of
single, two and three qubit gates. Using a variable length population
and universal stochastic selection procedure, a number of possible
solution circuits, with different number of gates can be obtained for
the same input matrix during different runs of GA. The given
algorithm has also been successfully implemented to obtain two and
three qubit Boolean circuits using Quantum gates. The results
demonstrate the effectiveness of the GA procedure even when the
search spaces are large.
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: In this study, an optimization of supersonic air-to-air ejector is carried out by a recently developed single-objective genetic algorithm based on adaption of sequence of individuals. Adaptation of sequence is based on Shape-based distance of individuals and embedded micro-genetic algorithm. The optimal sequence found defines the succession of CFD-aimed objective calculation within each generation of regular micro-genetic algorithm. A spring-based deformation mutates the computational grid starting the initial individualvia adapted population in the optimized sequence. Selection of a generation initial individual is knowledge-based. A direct comparison of the newly defined and standard micro-genetic algorithm is carried out for supersonic air-to-air ejector. The only objective is to minimize the loose of total stagnation pressure in the ejector. The result is that sequence-adopted micro-genetic algorithm can provide comparative results to standard algorithm but in significantly lower number of overall CFD iteration steps.
Abstract: In this work, Experimental tie-line results and
solubility (binodal) curves were obtained for the ternary systems
(water + acetic acid + methyl isobutyl ketone (MIBK)), (water +
lactic acid+ methyl isobutyl ketone) at T = 294.15K and atmospheric
pressure. The consistency of the values of the experimental tie-lines
was determined through the Othmer-Tobias and Hands correlations.
For the extraction effectiveness of solvents, the distribution and
selectivity curves were plotted. In addition, these experimental tieline
data were also correlated with NRTL model. The interaction
parameters for the NRTL model were retrieved from the obtained
experimental results by means of a combination of the homotopy
method and the genetic algorithms.
Abstract: Designing a simulated system and training it to optimize its tasks in simulated environment helps the designers to avoid problems that may appear when designing the system directly in real world. These problems are: time consuming, high cost, high errors percentage and low efficiency and accuracy of the system. The proposed system will investigate and improve the efficiency and accuracy of a simulated robot to choose correct behavior to perform its task. In this paper, machine learning, which uses genetic algorithm, is adopted. This type of machine learning is called genetic-based machine learning in which a distributed classifier system is used to improve the efficiency and accuracy of the robot. Consequently, it helps the robot to achieve optimal action.
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: In this paper, a novel associative memory model will be proposed and applied to memory retrievals based on the conventional continuous time model. The conventional model presents memory capacity is very low and retrieval process easily converges to an equilibrium state which is very different from the stored patterns. Genetic Algorithms is well-known with the capability of global optimal search escaping local optimum on progress to reach a global optimum. Based on the well-known idea of Genetic Algorithms, this work proposes a heuristic rule to make a mutation when the state of the network is trapped in a spurious memory. The proposal heuristic associative memory show the stored capacity does not depend on the number of stored patterns and the retrieval ability is up to ~ 1.
Abstract: The purpose of Grid computing is to utilize
computational power of idle resources which are distributed in
different areas. Given the grid dynamism and its decentralize
resources, there is a need for an efficient scheduler for scheduling
applications. Since task scheduling includes in the NP-hard problems
various researches have focused on invented algorithms especially
the genetic ones. But since genetic is an inherent algorithm which
searches the problem space globally and does not have the efficiency
required for local searching, therefore, its combination with local
searching algorithms can compensate for this shortcomings. The aim
of this paper is to combine the genetic algorithm and GELS (GAGELS)
as a method to solve scheduling problem by which
simultaneously pay attention to two factors of time and number of
missed tasks. Results show that the proposed algorithm can decrease
makespan while minimizing the number of missed tasks compared
with the traditional methods.
Abstract: A fuzzy classifier using multiple ellipsoids approximating decision regions for classification is to be designed in this paper. An algorithm called Gustafson-Kessel algorithm (GKA) with an adaptive distance norm based on covariance matrices of prototype data points is adopted to learn the ellipsoids. GKA is able toadapt the distance norm to the underlying distribution of the prototypedata points except that the sizes of ellipsoids need to be determined a priori. To overcome GKA's inability to determine appropriate size ofellipsoid, the genetic algorithm (GA) is applied to learn the size ofellipsoid. With GA combined with GKA, it will be shown in this paper that the proposed method outperforms the benchmark algorithms as well as algorithms in the field.
Abstract: This paper presents the speed regulation scheme of a small brushless dc motor (BLDC motor) with trapezoidal back-emf consideration. The proposed control strategy uses the proportional controller in which the proportional gain, kp, is appropriately adjusted by using genetic algorithms. As a result, the proportional control can perform well in order to compensate the BLDC motor with load disturbance. This confirms that the proposed speed regulation scheme gives satisfactory results.
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: In most of the popular implementation of Parallel GAs
the whole population is divided into a set of subpopulations, each
subpopulation executes GA independently and some individuals are
migrated at fixed intervals on a ring topology. In these studies,
the migrations usually occur 'synchronously' among subpopulations.
Therefore, CPUs are not used efficiently and the communication
do not occur efficiently either. A few studies tried asynchronous
migration but it is hard to implement and setting proper parameter
values is difficult.
The aim of our research is to develop a migration method which is
easy to implement, which is easy to set parameter values, and which
reduces communication traffic. In this paper, we propose a traffic
reduction method for the Asynchronous Parallel Distributed GA by
migration of elites only. This is a Server-Client model. Every client
executes GA on a subpopulation and sends an elite information to the
server. The server manages the elite information of each client and
the migrations occur according to the evolution of sub-population in
a client. This facilitates the reduction in communication traffic.
To evaluate our proposed model, we apply it to many function optimization
problems. We confirm that our proposed method performs
as well as current methods, the communication traffic is less, and
setting of the parameters are much easier.
Abstract: The design of a steam turbine is a very complex
engineering operation that can be simplified and improved thanks to
computer-aided multi-objective optimization. This process makes use
of existing optimization algorithms and losses correlations to identify
those geometries that deliver the best balance of performance (i.e.
Pareto-optimal points).
This paper deals with a one-dimensional multi-objective and
multi-point optimization of a single-stage steam turbine. Using a
genetic optimization algorithm and an algebraic one-dimensional
ideal gas-path model based on loss and deviation correlations, a code
capable of performing the optimization of a predefined steam turbine
stage was developed. More specifically, during this study the
parameters modified (i.e. decision variables) to identify the best
performing geometries were solidity and angles both for stator and
rotor cascades, while the objective functions to maximize were totalto-
static efficiency and specific work done.
Finally, an accurate analysis of the obtained results was carried
out.
Abstract: Flexible Job Shop Problem (FJSP) is an extension of
classical Job Shop Problem (JSP). The FJSP extends the routing
flexibility of the JSP, i.e assigning machine to an operation. Thus it
makes it more difficult than the JSP. In this study, Cooperative Coevolutionary
Genetic Algorithm (CCGA) is presented to solve the
FJSP. Makespan (time needed to complete all jobs) is used as the
performance evaluation for CCGA. In order to test performance and
efficiency of our CCGA the benchmark problems are solved.
Computational result shows that the proposed CCGA is comparable
with other approaches.
Abstract: Partitioning is a critical area of VLSI CAD. In order to build complex digital logic circuits its often essential to sub-divide multi -million transistor design into manageable Pieces. This paper looks at the various partitioning techniques aspects of VLSI CAD, targeted at various applications. We proposed an evolutionary time-series model and a statistical glitch prediction system using a neural network with selection of global feature by making use of clustering method model, for partitioning a circuit. For evolutionary time-series model, we made use of genetic, memetic & neuro-memetic techniques. Our work focused in use of clustering methods - K-means & EM methodology. A comparative study is provided for all techniques to solve the problem of circuit partitioning pertaining to VLSI design. The performance of all approaches is compared using benchmark data provided by MCNC standard cell placement benchmark net lists. Analysis of the investigational results proved that the Neuro-memetic model achieves greater performance then other model in recognizing sub-circuits with minimum amount of interconnections between them.
Abstract: Although Face detection is not a recent activity in the
field of image processing, it is still an open area for research. The
greatest step in this field is the work reported by Viola and its recent
analogous is Huang et al. Both of them use similar features and also
similar training process. The former is just for detecting upright
faces, but the latter can detect multi-view faces in still grayscale
images using new features called 'sparse feature'. Finding these
features is very time consuming and inefficient by proposed methods.
Here, we propose a new approach for finding sparse features using a
genetic algorithm system. This method requires less computational
cost and gets more effective features in learning process for face
detection that causes more accuracy.
Abstract: This paper presents a method of model selection and
identification of Hammerstein systems by hybridization of the genetic
algorithm (GA) and particle swarm optimization (PSO). An unknown
nonlinear static part to be estimated is approximately represented
by an automatic choosing function (ACF) model. The weighting
parameters of the ACF and the system parameters of the linear
dynamic part are estimated by the linear least-squares method. On
the other hand, the adjusting parameters of the ACF model structure
are properly selected by the hybrid algorithm of the GA and PSO,
where the Akaike information criterion is utilized as the evaluation
value function. Simulation results are shown to demonstrate the
effectiveness of the proposed hybrid algorithm.
Abstract: Recently, a lot of attention has been devoted to
advanced techniques of system modeling. PNN(polynomial neural
network) is a GMDH-type algorithm (Group Method of Data
Handling) which is one of the useful method for modeling nonlinear
systems but PNN performance depends strongly on the number of
input variables and the order of polynomial which are determined by
trial and error. In this paper, we introduce GPNN (genetic
polynomial neural network) to improve the performance of PNN.
GPNN determines the number of input variables and the order of all
neurons with GA (genetic algorithm). We use GA to search between
all possible values for the number of input variables and the order of
polynomial. GPNN performance is obtained by two nonlinear
systems. the quadratic equation and the time series Dow Jones stock
index are two case studies for obtaining the GPNN performance.
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.