Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Genetic Algorithm

Exploring the Effect of Dimensional Tolerance of the Inserts During Multi-Objective Optimization of Face Hard Milling Using Genetic Algorithm

S.K. Shihab

Engineering and Technology Journal, 2017, Volume 35, Issue 4, Pages 381-390

Surface roughness and dimensional deviation are critical quality dimensions of machined products and several machining parameters including tool insert dimensional tolerance affect them. Machining performance studies involving dimensional tolerance of the insert during machining, particularly hard face milling do not have considerable attention of the researchers. Therefore, the aim of the present work is to investigate the effect of the dimensional tolerance of the insert along with other machining parameters such as spindle speed, feed per tooth, and depth of cut on the roughness and dimensional deviation simultaneously. Experiments were conducted as per standard L18 mixed orthogonal array on a CNC vertical milling machine. Significance of machining parameters with respect to roughness and dimensional deviation was determined using Analysis of variance (ANOVA). Results revealed that among several machining parameters, feed per tooth greatly affects surface roughness and dimensional deviation. Optimum machining parameters that give minimum values of surface roughness and dimensional deviation simultaneously was obtained using Genetic Algorithm (GA).

Simulation Model of Al-Dura Electro-station Plant of 160 MW with Genetic Algorithm Method

Shaker H.Aljanabi; Alaa Siham Hamid

Engineering and Technology Journal, 2016, Volume 34, Issue 11, Pages 1928-1943

In the present paper, a thermodynamic analysis of Al-Dura, Baghdad station type (K– 160–13.34–0.0068), power plant has been carried out. The power plant system was simulated and a detailed parametric study undertaken. This study can be helpful to identify the plant site conditions that cause losses of useful energy taken place and also helpful to resolve some problems encountered in steam turbine, capacity unit. Developing nonlinear mathematical models based on system identification approaches during normal operation without any external excitation or disruption is always a hard effort, assuming that parametric models are available. This study included on using soft computing methods that would be helpful in order to adjust model parameters over full range of input–output operational data. In this case, the model parameters are adjusted by applying genetic algorithms as optimization methods. Comparison between the responses of the turbine – generator model with the responses of real system validates the accuracy of the proposed model in steady state and transient conditions. Simulation results shows that the efficiencies and feasibility of the developed model in term of more accurate and less deviation with the responses of read system in the steady and transient conditions, and the error of proposed function is less than 0.37%. This study presents the usage of the Cycle – tempo and Matlab/Simulink package to implement the model of the power plant. Finally, many recommendations have been suggested for improved plant performance.

Face Retrieval Using Image Moments and Genetic Algorithm

Wathiq Najah Abdullah; Yossra Hussain Ali

Engineering and Technology Journal, 2016, Volume 34, Issue 1, Pages 160-171

Content-based image retrieval has been developed in numerous fields. This provides more active management and retrieval of images than the keyword-based method. So the content based image retrieval becomes one of the liveliest researches in the past few years.In a given set of objects, the retrieval of information suggests solutions to search forthose in response to a particular description. The set of objects which can be considered are documents,images, videos, or sounds.
Moments can be viewed as powerful image descriptors that capture global characteristics of an image. The magnitude of the moment coefficients is said to be invariant under geometrical transformations like rotation which makes them suitable for most of the recognition applications.
This paper presents a method to retrieve a multi-view face from a large face database according to face image moments and genetic algorithm.
The GA is preferred for its power and because it can be usedwithout any specificinformation of the domain.
The experimental results concludes thatusing GA gives a good performance and it decreases the average search time to (56.44milliseconds) compared with (891.6 milliseconds) for traditional search.

Enhancing the Stability Performance of Iraqi National Super Grid System by Using UPFC Devices Based on Genetic Algorithm

Rashid H. Al-Rubayi; Abdulrazzaq F. Noori

Engineering and Technology Journal, 2013, Volume 31, Issue 10, Pages 1837-1853

The object of this work is to improve the stability of the Iraqi National Super Grid System (INSGS) by installing Unified Power Flow Controller (UPFC) devices in different optimal locations under fault condition and comparing the results with those of without FACTS under the same condition.The optimal location of the FACTS device was specified based on Genetic Algorithm (GA) optimization method, it was utilized to search for optimum FACT parameters setting and location based objective function that depends on the power and voltage as a fitness constraints.MATLAB was used for running both the GA program and Power System Analysis Toolbox (PSAT) as Graphical User Interface, Newton Raphson method also used for solving the load flow of the system and the Trapezoidal method for the non-linear differential equations.The system that has been implemented is INSGS 11-machine, 24-bus, 39 (400kV) overhead transmission lines.The GA program is applied for the Iraqi grid system which is complicated.The results obtained showed that the installation of UPFC devices at the optimal locations of the Iraqi grid gives an improvement in the stability by damping the voltage and rotor angle oscillations after subjected to the three phase fault to ground at different locations and different cases (temporary fault, permanent fault).A comparison has been made between these different cases based on the durations of the tested faults, and with the UPFC devices installed in the system, it can remain stable for longer time than without UPFC during fault condition.

Optimization of Al-Doura Catalytic Naphtha Reforming Process Using Genetic Algorithm

Zaidoon M. Shakoor

Engineering and Technology Journal, 2013, Volume 31, Issue 7, Pages 1276-1296

Optimization of Al-Doura catalytic naphtha reforming process was done using genetic algorithm. The objective of optimization is maximization yield of the aromatics in order to increase the octane number of reformate.
One-dimensional steady-state mathematical model was made to study the effect of feedstock composition, feed temperature, total pressure and hydrogen to hydrocarbon feed ratio on the reformate compositions. Detailed kinetic model was developed to describe the reaction kinetic, the model involving 29 components, 1 to 11 carbon atoms for n-paraffins, 5 to 10 carbon atoms for iso-paraffins and 6 to 11 carbon atom for naphthenes and aromatics with 83 reactions. Using Genetic Algorithm, 186 parameters of the proposed kinetic model were predicted depending on plant results collected over two months from Al-Doura reforming process which located in the south of Baghdad. The validity of the kinetic model was approved by comparing the results of developed kinetic model with the actual process results.
Genetic algorithm was used again to optimize the commercial reforming process depending on reformate compositions. Optimization was carried out in temperature range between 450 to 520°C; total pressure range 5 to 35 bar; hydrogen to hydrocarbon ratio 3 to 8 and by varying the percentage of catalyst for each one of four reactors. Optimization results shows that, it’s possible to increase the aromatics composition in reformate from 63.42 % in actual unit to 70.89 % by changing the design variables and operating conditions.

Multi-Stages for Tuning Fuzzy Logic Controller (FLC) Using Genetic Algorithm (GA)

Ammar G. Samir

Engineering and Technology Journal, 2013, Volume 31, Issue 6, Pages 1166-1181

In this paper, a study on tuning of fuzzy logic controller (FLC) using genetic algorithm (GA) for controlling an armature controlled DC motor as an example of linear plant and for controlling nonlinear plant as another example is performed. There are different ways in which a FLC can be tuned, like: tuning the scaling gains, Rule Base (RB), and Data Base (DB) represented by type of membership functions or parameters of membership functions used. The tuning process in this paper includes a multi-stage tuning represented by searching the good scaling gains, RB, and DB then a combination of multi-stage (CMS) tuning methods using Genetic Algorithm (GA) based on a fitness function that is defined in terms of performance criterion (Integral of Squared Error ISE). The performances of these tuning stages are evaluated and a comparison between them has been introduced using linear and nonlinear plants.

Particle Swarm Optimization and Genetic Algorithm for Tuning PID Controller of Synchronous Generator AVR System

Fadhil A. Hassan; Lina J. Rashad

Engineering and Technology Journal, 2011, Volume 29, Issue 16, Pages 3256-3270

Proportional Integral Derivative (PID) controllers are widely used in
many fields because they are simple and effective. Tuning of the PID
controller parameters is not easy and does not give the optimal required
response, especially with non-liner system. In the last two decades many
intelligent optimization techniques were took attention of researchers like:Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques. This paper represented the non-linear mathematical model and simulation of the synchronous generator with closed loop PID controller of AVR system. The traditional PID tuning technique is proposed as a point of comparison. Two of intelligent optimization techniques: PSO and GA are proposed in this paper to tune the PID controller parameters. The obtained results of the closed loop PSO-PID and GA-PID controller response to the unit step input signal shows excellent performance with respect to the traditional trial and
error tuning of the PID controller.

Particle Swarm Optimization for Total Operating Cost Minimization in Electrical Power System

Mohammed H. al-khafaji; Shatha S.Abdulla al-kabragyi

Engineering and Technology Journal, 2011, Volume 29, Issue 12, Pages 2539-2550

This paper presents solution of economic dispatch problem via a particle swarm optimization algorithm (PSO). The objective is to minimize the total generation fuel cost and keep the power flows within the security limits. The PSO is simple in concept, easy in implementation .It does not require any derivative information, sure and fast convergence, Moreover; it is needs less computational time than other heuristic methods. These features increase the applicability of the PSO, particularly in power system applications .The effectiveness of the proposed algorithm is demonstrated on the IEEE 37-bus system and their performances are compared with the results of genetic algorithm (GA). The results show that PSO can converge to optimum solution with higher accuracy in comparison with GA.

An Efficient Approach Combining Genetic Algorithm and Neural Networks for Eigen Value Grads Method (EGM) In Wireless Mobile Communications

Mohammed Hussein Miry

Engineering and Technology Journal, 2011, Volume 29, Issue 13, Pages 2590-2600

The objective of this paper is combining Genetic Algorithm and Principal
Component Analysis (PCA) neural network for Eigenvalue Grads Method (EGM)
to estimate the number of sources in wireless mobile communications. The
Eigenvalue Grads Method (EGM) is a popular method for estimation the number
of sources impinging on an array of sensors, which is a problem of great interest in
wireless mobile communications. This paper proposed a new system to estimate
the number of sources by applying the output of genetic algorithm and PCA neural
network with Complex Generalized Hebbian algorithm (CGHA) to EGM
technique. In the proposed model, the initial weight and learning rate values for
CGHA neural network can be selected automatically by using Genetic algorithm.
The result of computer simulation for proposed system showed good response by
fast converge speed for neural network , efficiency and yield the correct number of
the sources. The important feature of new system is that, the PCA of covariance
matrix are calculated based on CGHA neural network instead of determining the
covariance matrix because computation of covariance matrix is time consuming.

Hybrid Simple Genetic Algorithm (HSGA) and the Effect of using Fitness Functions for Layout Problem

Fadhela Sabry Abu-Almash; Baidaa Abd-Alkhalik; Firas Ali Hashim

Engineering and Technology Journal, 2011, Volume 29, Issue 6, Pages 1166-1175

In this research there is a wide study about Hybrid Genetic algorithm was
presented in addition to Varity in fitness functions and there are effect on used
Results occur by using disjoint algorithm with genetic algorithm. We applied on
two matters which are the (10) ten objects and the more complex , the (30) thirty
objects . This way called hybrid simple genetic algorithm. This way developed to
solve this subject of different objects layout.

Optimal Size and Location of Distributed Generators using Intelligent Techniques

Rashid H. Al-Rubayi; AzharM. Alrawi

Engineering and Technology Journal, 2010, Volume 28, Issue 23, Pages 6623-6633

One of the modern and important techniques in electrical distribution
systems is to solve the networks problem of service availability, high loss and low voltage stability by accommodating small scaled de-centralized generating units in these networks, which is known as distributed generation (DG). The Genetic Algorithm (GA) technique is dedicated in this work to find the optimal DG locations, and then optimally allocate units in order to maximize the penetration
level, minimize loss, and improve voltage stability

Improving the Accuracy of Static Relative GPS Positioning using Genetic Algorithm

Farazdak Rafeek Yasien

Engineering and Technology Journal, 2010, Volume 28, Issue 21, Pages 6306-6314

Over the years, the Global Positioning System (GPS) has evolved to become an important navigational and positional system and is widely used across the world. The system promises high accuracies if the navigational signals transmitted by the GPS satellites are observed accurately. The modeling of a single point and relative point determination of user position includes pseudorange measurements. Taylor series is used to linearize the nonlinear model. Two methods are used to estimate the three dimensional user position: Recursive Least Square (RLS) method and continuous Genetic Algorithm (GA) method.
Real data is used and results show that the GA enhances the estimation of user position more than the RLS by high error minimization and the minimum number of available satellites needed. RLS with three satellites give an error that exceeds the allowable limits, while GA gives an acceptable error. Relative positioning method is more accurate than the point positioning method for both RLS and GA methods..

A Proposed Genetic Algorithm for Multicast Routing

Muna Mohammed Al-Nayar; Abdul Kareem Mahmod Shukri

Engineering and Technology Journal, 2010, Volume 28, Issue 15, Pages 4992-4999

Many Internet applications (such as video conferences) are one-to-many
or many-to-many, where one or multiple sources are sending to multiple receivers. These applications need certain Quality of Services to be guaranteed in underlying network. This paper presents a genetic multicast routing algorithm which finds the low-cost multicasting tree from a designated source to multiple destinations with Quality of Service (QoS) (i.e., bandwidth and end-to-end delay) constraints.
Experimental results show that the proposed algorithm finds the minimum-cost multicast routing tree while satisfying QoS constraints and could finally converge to the global optimal solution for a large-scale network.

Chattering Attenuation of Sliding Mode Controller Using Genetic Algorithm and Fuzzy Logic Techniques

Farzdaq R. Yasien; Mina Q. Kadhim

Engineering and Technology Journal, 2009, Volume 27, Issue 14, Pages 2595-2610

Sliding Mode Controller design provides a systematic approach to the
problem of maintaining stability and consistent performance in the face of modeling imprecision. The major drawback that sliding mode control suffers from is the chattering phenomenon, which is a zigzag motion along the sliding surface caused by the high frequency motion on the sliding surface. This phenomenon is an undesirable property since it excites unmodeled dynamics and results in tear and wears in the mechanical systems. In this work several methods are proposed to
reduce the chattering. One of these methods is to use the boundary layer solution to smooth the hard switching signal. This solution is compared to another one represented by involving the intelligent systems to enhance the performance of the sliding mode controller system like involving the Genetic Algorithm (GA) and the
fuzzy tuning technique. GA has proved its efficient ability to attenuate chattering and reduce the hitting time compared to other methods.

High Resolution Direction-of-Arrival Estimation Using Genetic Algorithm

Ali Abdul-Elah Noori; Eyad I. Abbas

Engineering and Technology Journal, 2009, Volume 27, Issue 9, Pages 1746-1754

This paper presents an application of a performance analysis of a genetic algorithm GA developed for extraction of the directions of arrival DOA of several signals impinging on uniform linear arrays. The first part of this paper describes the maximum likelihood ML technique of direction of arrival estimation with genetic algorithm. The second part presents some illustrative simulation cases of ML-DOA estimation by using GA. Results are statistically analyzed in order to conclude from it the algorithm's accuracy and reliability.

Study of The Optimization Condition Of Batch Sterilization Using Genetic Algorithm

J.Mohammed; Thamer; F.Tobia; Ittehad; N.F.Al-Obaidi; Salah

Engineering and Technology Journal, 2009, Volume 27, Issue 6, Pages 1083-1092

The present work is designed to study the parameters interaction of sterilization
processes in batch bioreactors (fermentors of volume 120 liter with medium of 56784 kg).
he parameters include the effects of sterilization temperatures (117-126
C), time of
heating, time of holding, and time of cooling on removal of all organisms, and degradation
degree of medium. Direct steam was used for heating at different temperatures ranged
from 120
C to 180
C. The B.Stearothermophilus was selected as the present contaminants.
Another bioreactor of volume (56828) liter was studied at 121
C for the same contaminant
and compared with actual data.
This study is achieved by designed procedure and simulation program useful for
the optimization of batch sterilization cycle in large-scale fermentors. The method of
optimization used is Genetic Algorithm (GAs) which uses probability to find the optimum
condition for the sterilization cycle and to find Del factor; which is the reduction value of
initial to final number of microorganisms, and then evaluate the cost which depend on
amount of steam consumed in the sterilization processes
Graphical relations was indicated that as fermentor size increase, the time of
heating also increase. For low temperature the time of holding was increased and for high
temperature the time of holding was decreased. Also these relations were investigated the
best conditions between holding time and Del factor for degradation at different