Eastern University Digital Library

Dewan Muktadir- Al- Mukit, A. Z. M. Shafiullah

Does monetary policy have impact on stock market performance? An econometric approach

Nazrul Islam, Md Wahidul Habib, Rukshana Karim Pathan

1The Journal of Global Commerce, Vol. 2, Number 4, Fall 2010
Factors Affecting the Recruitment and Selection Process of Private
Commercial Banks in Bangladesh
Dr. Nazrul Islam
Professor, Department of Business Administration
East West University, Dhaka, Bangladesh
&
Dr. Md Wahidul Habib
Department of Business Administration
East West University, Dhaka, Bangladesh
&
Rukshana Karim Pathan
Lecturer, Department of English
Narayanganj College, Narayanganj, Bangladesh
Abstract
There is a paramount importance of appropriateness in recruitment and selection for the
desired performance of the private commercial banks in Bangladesh. It is the
determinant of the performance of the banks that leads to the growth and development of
the banks in future. However, this aspect of management is often ignored by HRM
department of the banks. The directors of the banks are unable to come out from their
circles like family, friends, and relatives in recruitment decision. The people involved in
the process influence the whole process of recruitment and selection for their personal
interest. Candidates’ understanding and mindset for getting jobs sometimes create a kind
of force that influences the process. People from the monitoring bank often create
pressure on recruitment and selection process. External factors like political, economic,
social, and legal also have strong influence on the selection process. As such, this study
aims at identifying the factors affecting the recruitment and selection process of private
sector commercial banks in Bangladesh. It includes the manpower planning and selection
process of the banks that is most prominent and stable sector compared to other
industrial sectors of Bangladesh. A number of factors affecting recruitment and selection
process of the banks were identified through literature review. Multivariate analysis
techniques such as, Factor Analysis and Multiple Regression Analysis were performed to
identify the relationships between recruitment and selection process and the factors.
Factor Analysis was conducted to identify the factors affecting the recruitment and
selection process of the banks. Multiple Regression Analysis was performed to identify
the relationship between the factors and recruitment and selection process. Results show
that the factors including family pressure of the directors, interference of the people
involved in the process, political interference, capability of the interviewer to design the
tests, and candidates’ perception regarding the possibility of getting job have significant
influence on the recruitment and selection process in the private commercial banks of
Bangladesh. It was concluded from the study that the factors affecting recruitment and
selection process of the banks are unable to fulfill the gap between the qualifications of
the candidates and the job requirements of the banks.
21. Background
There is a paramount importance of recruitment and selection in an organization’s human
resource planning and its competitive strength. Increasing flexibility in the labor market,
recruitment is becoming more and more important in every business now a days.
Competent human resources at the right positions in the organization are a vital resource.
It can be a core competency for the organization in future. The prime objective of the
recruitment and selection process is to obtain the number and quality of employees that
can be selected in order to help the organization to achieve its goals and grow in future.
As such, recruitment and selection helps to create a pool of prospective employees for the
organization so that the management can select the right candidate for the right job from
the pool. Recruitment serves as the first step in fulfilling the needs of organization for a
competitive, motivated, and flexible human resource that can help achieve its objectives.
Bangladesh is a highly populous country of the world having about 150 million people.
About one third of the total population is employed. The total labor force of the country
was 53.70 million in 2009 with 2.70% growth rate a year on average. Among this
population, unemployed population in 2009 was 2.70 million that is the increase of 28%
over 2006 (Bangladesh Bureau of Statistics, 2009). However, the creation of job
opportunities in different sectors is simply incompatible to this increase of unemployed
people.
Banking sector is one of the largest sectors of employment in Bangladesh especially in
private banks. There are 29 private commercial banks operating in the country. The
names of the banks are: AB Bank Limited, Al-Arafa Islami Bank Ltd, Aziz Co-op
Commerce & Finance Bank Ltd, Bangladesh Commerce Bank Ltd, Bank Asia Limited,
Dhaka Bank Limited, Dutch-Bangla Bank Limited, Eastern Bank Limited, Exim Bank
Limited, First Security Islami Bank Limited, IFIC Bank Limited, Islami Bank
Bangladesh Limited, Jamuna Bank Limited, Mercantile Bank Limited, Mutual Trust
Bank Limited, National Bank Limited, National Credit & Commerce Bank Ltd, One
Bank Limited, Premier Bank Limited, Prime Bank Limited, Pubali Bank Limited,
Shahjalal Bank Limited, Social Islami Bank Limited, South East Bank Limited, Standard
Bank Limited, The Trust Bank Limited,  United Commercial Bank Limited, The City
Bank Limited and Uttara Bank Limited (http://www.bdgateway.org). In Bangladesh
society, job in bank is acceptable and prestigious in terms of salary and job itself. As
such, there is a very high pressure on the recruitment and selection process in these
banks.
Recruitment and selection is very much important for any organization like bank as it
determines the future performance of the people recruited and performance of the bank as
well. Due to the pressure from the supply of unemployed people, recruitment and
selection process is substantially influenced by the different actors and factors. The
reason for this interference may be attributed by not only the inadequacy of the jobs but
also the supply of graduates in the labor market. Observations show that this influence is
360 degree influence system. Sometimes, recruitment of people becomes ineffective and
impossible to the HR people and they are hardly able to recruit right person for the right
3job. The influencing factors may be classified into two types such as internal and
external.
Figure 1 Factors Affecting Recruitment and Selection Process of the Organization1
The internal factors are mainly related to recruitment policy, human resource planning,
size of the firm, cost of recruitment, and growth and expansion. The external factors are:
supply and demand, labor market, image or goodwill, political/social/legal environment,
unemployment rate, and competitors. As such, recruiter needs to be very much conscious
and careful about the factors/actors that have potential influence on the recruitment and
selection process. More specifically, top management or directors interference, recruiter
or HR department people, control authority or Bangladesh Bank, political influence,
quality of selection process, advertising or promotional effort of the bank, and
candidates’ perception regarding the job. Brooks, et.al., (2009) identified that the people
differentiate between options more when they consider them simultaneously than when
they view them separately. A number of factors induce them to differentiate in this regard
which are broadly related to internal and external factors. The beliefs of the HR
professionals also have influence on the recruitment and selection process. Study shows
that the use of psychometric tests by practitioners is a function of many things: their
education and experience, the country they work in, test publisher marketing, popular
articles about testing, litigation, etc. As such, some tests remain very popular among
practitioners despite being little used in research (Furnham, 2008).
1 Source: http://www.scribd.com/doc/12460528/Recruitment-Factors-at-Bank dated 15 September 2010
Factors Affecting Recruitment and
Selection Process
External Factors:
1. Supply and demand
2. Labor market
3. Image / Goodwill
4. Political/Social/Legal
Environment.
5. Unemployment rate
6. Competitors
Internal Factors:
1. Recruitment policy
2. Human resource planning
3. Size of the Firm
4. Cost of recruitment
5. Growth and Expansion
4Recruitment policy of an organization specifies the specific objectives of recruitment and
selection. It provides a framework for implementation of recruitment program in the
organization by filling up vacancies with best qualified people. Effective human resource
planning helps in determining the gaps present in the existing manpower resource of the
organization. How many people will be recruited in what positions are also determines by
the recruitment and selection process. The size of the firm is another important factor in
the recruitment and selection process. Organization will think to employ more personnel
if it plans to increase its operations and expand its business in order to handle the
extended operations. Cost is another factor that has influence on the recruitment and
selection process. As such, organizations try to employ the source of recruitment that
incurs a lower cost of recruitment to the organization. For expanding decision of the
organization, more people will be required to recruit in future.
On the other hand, the availability of manpower both inside and outside the organization
is an important determinant in the recruitment and selection process. Company depends
upon internal sources if it has a demand for more professionals and there is limited
supply in the market. In such situation, company should focus more on training and
development of the employees. Employment conditions in the community where the
organization is located influences the recruiting recruitment and selection process of the
organization. Surplus manpower in the labor market assembles together when there is an
advertisement given by a company. Company image and goodwill is another factor affect
recruitment and selection process of the company. Government rules prohibiting
discrimination in hiring and employment have direct impact on recruitment and selection
process of the organization. The militancy of trade unions play important role in
recruitment and selection process. There is often oversupply of qualified labor which in
turn leads to unemployment if the company is unable to create new jobs. The recruitment
policies of the competitors also affect the recruitment and selection process of the
company.
Keeping this in mind, this study focuses on to exploring the factors affecting the
recruitment and selection process of private commercial banks operating in Bangladesh.
2. Research Methods
This study attempts to identify the factors affecting the recruitment and selection process
of private commercial banks in Bangladesh. To conduct the study, primary and secondary
sources of information were used. Primary data was collected from the HR officials of
the banks under study. One hundred forty five HR officials from 29 private commercial
banks, five from each bank, were selected and interviewed as sample respondents. All
respondent were officers in their designation. A survey has been conducted among the HR
officials of the mentioned banks to collect information about the factors affecting
recruitment and selection process with the help of a structured questionnaire.
A structured questionnaire in a 5-point scale was developed for the variables affecting
recruitment and selection process of the private commercial banks. A five-point scale
5ranging from 1 to 5 with 1 indicating strongly disagree and 5 indicating strongly agree
was used in this regard.
This study identified items affect recruitment and selection process of bank through
literature review and from the pilot survey. Ten HR managers were surveyed and the
factors identified by them were included in the questionnaire. The variables affecting the
recruitment and selection process of private commercial banks in Bangladesh are:
recruitment policy, human resource planning, size of the firm, cost of recruitment, growth
and expansion, supply and demand, labor market, image or goodwill,
political/social/legal environment factor, unemployment rate of the country, competitors
manpower need, top management or directors interference, recruiter or HR department
people, control authority or Bangladesh Bank interference, quality of selection process,
advertising or promotional effort of the bank, candidates’ perception regarding the job,
reference used by the candidate, experience of the candidate, educational qualifications,
interview performance, and allegation / complain against the candidate. The dependent
variable was identified as the overall response of the respondents.
The dependent variable is explained by recruitment and selection process of the banks.
The survey has been conducted with the assistance of MBA students of East West
University from January 2010 to July 2010. The interviewers were trained on the
variables representing the questionnaire for data collection before starting interview.
Along with descriptive statistics, multivariate analysis techniques such as, Factor
Analysis and Multiple Regression Analysis were used to analyze the relationships of the
factors affecting the recruitment and selection process of the private commercial banks in
Bangladesh. A Principal Factor Analysis with an orthogonal rotation (Varimax)2 using
the SPSS statistical package was performed on the survey data and was used to separate
the factors.
2 Varimax rotation is an orthogonal rotation of the factor axes to maximize the variance of the squared
loadings of a factor (column) on all the variables (rows) in a factor matrix, which has the effect of
differentiating the original variables by extracted factor. Each factor will tend to have either large or small
loadings of any particular variable. A varimax solution yields results which make it as easy as possible to
identify each variable with a single factor. This is the most common rotation option.
63. Results and Discussions
Table 1 Communalities of the Items
Extraction
1. Recruitment policy .694
2. Human resource planning .731
3. Size of the firm .723
4. Cost of recruitment .555
5. Growth and expansion .622
6. Supply and demand .566
7. Labor market condition .454
8. Image or goodwill of the bank .763
9. Political/social/legal environment factor .519
10. Unemployment rate .694
11. Competitors policy .728
12. Top management or directors interference .489
13. Recruiter or HR department people behavior .592
14. Control authority or Bangladesh Bank interference .522
15. Quality of selection process .559
16. Advertising or promotional effort of the bank .627
17. Candidates’ perception regarding the job/position .573
18. References used by the candidate .498
19. Experience .486
20. Educational qualifications .589
21. Interview performance .592
22. Allegation / complain against the candidate .611
Extraction Method: Principal Component Analysis.
Table 2 Total Variance Explained by the Factors
Factors
Initial Eigenvalues
Total
% of
Variance
Cumulative
%
Recruitment policy and HR planning 6.134 27.880 27.880
Unemployment rate 2.254 10.245 38.125
Quality of selection process and advertisement 1.420 6.455 44.580
Job perception and reference 1.219 5.543 50.122
Experience and Educational Qualifications 1.152 5.237 55.359
Interview performance 1.008 4.582 59.941
Extraction Method: Principal Component Analysis.
Table 3 Rotated Component Matrix(a): Factor Loadings
Items Factors
1 2 3 4 5 6
1. Recruitment policy .767 .052 .233 .153 .138 .076
2. Human resource planning .726 -.045 .064 .057 -.084 .103
73. Size of the firm .698 .114 -.001 .277 -.009 .121
4. Cost of recruitment .692 .011 .179 .163 .245 -.158
5. Growth and expansion .672 .083 -.129 -.066 -.093 .033
6. Supply and demand .630 -.009 .410 -.036 .167 .367
7. Labor market condition .594 .161 -.168 .507 -.004 .170
8. Image or goodwill of the bank .535 -.184 .100 .059 .443 .209
9. Political/social/legal environment factor .532 .006 .186 .273 .354 -.050
10. Unemployment rate .142 .761 -.076 .140 .005 .056
11. Competitors policy .012 .630 .005 .082 .092 .272
12. Top management or directors
interference -.011 .576 .346 .004 .240 -.225
13. Recruiter or HR department people
behavior .169 .488 .184 .067 .035 .465
14. Control authority or Bangladesh Bank
interference .136 -.462 .252 .379 .062 -.103
15. Quality of selection process .119 .106 .855 -.007 -.041 -.065
16. Advertising or promotional effort of the
bank .128 -.393 .520 .284 -.025 .257
17. Candidates’ perception regarding the
job/position .067 .008 .014 .760 .025 .097
18. References used by the candidate .265 .198 .100 .554 .248 .100
19. Experience -.064 .234 -.171 .095 .796 -.008
20. Educational qualifications .480 .012 .306 .104 .569 .253
21. Interview performance .061 .161 -.112 .150 .012 .739
22. Allegation / complain against the
candidate .472 .034 .217 .034 .264 .473
Extraction Method: Principal Component Analysis.  Rotation Method: Varimax with Kaiser
Normalization.
a  Rotation converged in 11 iterations.
Table 4 Model Summary
Mode
l R R Square
Adjusted R
Square Std. Error of the Estimate
1 .652(a) .426 .409 .85774
a  Predictors: (Constant), REGR factor score   6 for analysis 1  , REGR factor score   2 for analysis 1  ,
REGR factor score 3 for analysis 1  , REGR factor score   4 for analysis 1  , REGR factor score   5 for
analysis 1  , REGR factor score   1 for analysis 1
Table 5 ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 112.829 6 18.805 25.560 .000(a)
Residual 152.292 207 .736
Total 265.121 213
a  Predictors: (Constant), REGR factor score   6 for analysis 1  , REGR factor score   2 for
analysis 1  , REGR factor score   3 for analysis 1  , REGR factor score   4 for analysis 1  , REGR
factor score   5 for analysis 1  , REGR factor score   1 for analysis 1
b  Dependent Variable: Overall
8Table 6 Coefficients(a)
Factors
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 3.529 .059 60.169 .000
Recruitment policy and HR
planning .642 .059 .573 10.864 .000*
Unemployment rate
.118 .059 .106 2.019 .045*
Quality of selection process
and advertisement .126 .059 .112 2.128 .034*
Job perception and reference
.165 .059 .148 2.811 .005*
Experience and Educational
Qualifications .058 .060 .051 .966 .335
Interview performance
.252 .059 .224 4.257 .000*
a  Dependent Variable: Overall
* Significant at 5% level of significance
4. Conclusions and Recommendations
5. References
1. Brooks, E. M., Guidroz, M. A., & Chakrabarti, M., (2009), Distinction Bias in
Applicant Reactions to Using Diversity Information in Selection, International
Journal of Selection and Assessment, Vol. 17 (4): 377-390.
2. Furnham, A., (2008), HR Professionals’ Beliefs About, and Knowledge of,
Assessment Techniques and Psychometric Tests, International Journal of
Selection and Assessment, Vol. 16 (3): 299-305.


Factors Affecting the Recruitment and Selection Process of Private Commercial Banks in Bangladesh

Gour Chandra Saha, Nazrul Islam

Modeling information systems for technology strategy formulation

M. G. Rabbani, S. Elangovan, J. B. X. Devotta

Ann controlled  SMES unit to improve damping of local area oscillations in power system

A. B. M. Nasiruzzaman,  M. G. Rabbani, M. R. I. Sheikh,  M. S. Anower, Shubhashis K. Shil, Md. Ashraful

International Conference on Electronics, Computer and Communication (ICECC 2008) 
University of Rajshahi, Bangladesh 
 
ISBN 984-300-002131-3 
 
A Genetic Algorithm Based Approach for Solving Optimal Power Flow Problem  
 
A. B. M. Nasiruzzaman1, M. G. Rabbani2, M. R. I. Sheikh3, M. S. Anower4, Shubhashis K. Shil5, Md. Ashraful 
Haque6 
1-2,4EEE & 5-6CSE  Department, Rajshahi University of Engineering & Technology, 6204, Bangladesh 
3Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido, 090-8507, Japan 
E-mail: nasir_zaman_eee@yahoo.com1 
 
 
Abstract: The objective of this paper is to evolve 
simple and effective methods for the optimal power 
flow (OPF) problem in thermal units, which are 
capable of obtaining optimal power generations for 
a large-scale system. In optimization, GA has some 
well-known advantages and disadvantages. The 
classical (derivative based) method is applied as 
economic dispatch problem with transmission 
capacity constraints in literature. In this study, a 
similar investigation for economic dispatch is 
considered using simple Genetic Algorithm. The 
performance of results obtained with both of the 
methods is nearly same but from the viewpoint of 
calculation time GA is much superior and its 
effectiveness increases as the constraints of the 
problem are increased.  
Key Words: Genetic Algorithm, Optimal Power 
Flow. 
 
1.     INTRODUCTION 
In a practical power system, the power plants are not 
located at the same distance from the centre of loads 
and their fuel costs are different. Also under normal 
operating conditions, the generation capacity is more 
than the total load demand and the losses. Thus, there 
are many options for scheduling generation. In an 
interconnected power system the objective is to find 
power scheduling of each power plant in such a way 
as to minimize the operating cost. This means that 
the generator’s power is allowed to vary within 
certain limits so as to meet a particular load demand 
with minimum fuel cost. This is called the optimal 
power flow problem. The OPF is used to optimize 
the power flow solution of large scale power system. 
This is done by minimizing selected objective 
function while maintaining an acceptable system 
performance in terms of generator capability limits 
and the output of compensating devices. The 
objective functions, also, known as cost functions, 
may represent economic costs, system security, or 
other objectives.  
 
The OPF has been studied by many researchers and 
methods have been presented in several classical 
papers [2], [3], and [4], and in well established 
textbooks [5], and [6]. These classical methods 
require specific assumptions on the objective 
function such as continuity, differentiability. 
Moreover, these methods are slowly convergent.  
Genetic Algorithms (GA) are general search 
technique based on the mechanism of natural 
selection and natural genetics. In GA, genetic strings 
represent search points. The search process uses 
probabilistic transition rules instead of deterministic 
ones as in traditional optimization method. The 
search is conducted over a population of solutions. 
Moreover, no specific assumptions on the objective 
function, such as continuity, differentiability, etc. are 
required in GA.    
Genetic Algorithm is different from traditional or 
conventional methods in four ways: 
1. GA work with a coding of the parameter 
set, not the parameters themselves. 
2. GA search from a population of points, not 
a single point. 
3. GA use payoff (objective function) 
information, not derivatives or other auxiliary 
knowledge. 
4. GA use probabilistic transition rules, not 
deterministic rules. 
In several papers [1] the optimal power flow problem 
is solved using GA considering a power plant 
composed of six generators.  
In our paper, analysis is done for any number of 
buses with any number of generators.  
The OPF problem has a nonlinear objective function 
having many variables and constraints. In this paper 
GA has been used to solve the OPF problem as it 
completely replaces classical mathematical 
programming methods and also it is easy to 
implement as an optimization procedure.       
In view of the above, the main thrust of the research 
work presented in this paper is to propose a new but 
effective method for solving OPF using GA. The 
main objectives of the research work presented are as 
follows: 
 
1.   To present a systematic approach for the design 
of a new method for solving optimal power flow 
problem using GA. 
2.  To compare the performance of the GA based 
method with the conventionally derivative based 
methods [1], [2], [4], and [5]. 
 
476
International Conference on Electronics, Computer and Communication (ICECC 2008) 
University of Rajshahi, Bangladesh 
 
ISBN 984-300-002131-3 
 
2.    MATHEMATICAL MODEL 
Mathematical models are of fundamental importance 
in understanding any physical system. This section 
focuses on the modeling of OPF problem and its 
formulation. The factors influencing power 
generation at minimum cost are operating 
efficiencies of generators, fuel cost, and transmission 
losses. The most efficient generator in the system 
does not guarantee minimum cost as it may be 
located in an area where fuel cost is high. Also if the 
plant is located far from the load center, transmission 
losses may be considerably higher and hence the 
plant may be overly uneconomical. Hence the 
problem is to determine the generation of different 
plants such that the total operating cost is minimum. 
In all practical cases, the fuel cost of generator i with 
power output iP can be represented as a quadratic 
function of real power generation 
2
iiiiii PPC γβα ++=  
where, ,, ii βα and iγ represent unit cost 
coefficients. 
The economic dispatch problem is to find the real 
power generation for each plant such that the 
objective function (i.e., total production cost) as 
defined by the equation 
∑
=
++=
n
i
iiiiit PPC
1
2γβα    
is minimum subject to the inequality constraints 
given by  
(max)(min) iii PPP ≤≤    gni ,......,2,1=  
where (min)iP and (max)iP are the minimum and 
maximum generating limits respectively for plant i, 
subject to the constraint that generation should equal 
total demand plus losses, i.e., 
∑
=
+=
gn
i
LDi PPP
1
 
where tC is the total production cost, iC is the 
production cost of the i-th plant, iP is the generation 
of i-th plant, DP is the total load demand, and gn is 
the total number of displaceable generating plants 
and PL is the total transmission loss as a quadratic 
function of the generator power outputs given by 
Kron’s loss formula  
∑∑∑
== =
++=
gg g n
i
ii
n
i
n
j
jijiL BPBPBPP
1
000
1 1
 
The coefficients ijB are called loss coefficients. 
3.    GENETIC ALGORITHM 
 
Genetic algorithms are computerized search and 
optimization algorithms based on the mechanics of 
natural genetics and natural selection. They combine 
the survival of the fittest among string structures with 
a structured yet randomized information exchange to 
form search algorithm with some of the innovative 
flair of human search. In every generation, a new set 
of artificial creatures (strings) is created using bits 
and pieces of the fittest of the old; an occasional new 
part is tried for good measure. While randomized, 
genetic algorithms are no simple random walk. They 
efficiently exploit historical information to speculate 
on new search points with expected improved 
performance [7], and [8]. 
 
3.1 General Structure of GA 
The sequential steps for searching optimal solution 
of PSS parameters using GA is shown in Fig.1. 
 
 
Fig. 1  Computational flow chart. 
 
3.2 Design Methodology 
The design problem can be formulated as the 
following optimization problem:  
 
1.  Minimize the objective function 
2
iiiiii PPC γβα ++=  subjected to 
constraint maxmin PPP i ≤≤ . Here the fitness 
477
International Conference on Electronics, Computer and Communication (ICECC 2008) 
University of Rajshahi, Bangladesh 
 
ISBN 984-300-002131-3 
 
function )0(ii CCf −= is taken and it is to be 
minimized. 
2. An initial population of 256 individuals is 
generated each of 8 bits with the following 
equations. So, N=8. 
(a) First the accuracy with following 
parameters is chosen: 
12 −
−= N
LU XXAccuracy ; 
where maxPX
U = and minPX L = .  
(b) Chromosome/Individual value                       
= Accuracy*Si where Si=0,1,2,…,255. 
3.  From this initial population of 256, 128 
individuals are taken randomly for every 
generation. 
4. Roulette Wheel Method of reproduction is used.   
5. Single cross-site of crossover is used with 
crossover rate 0.5 and mutation rate 0.05. 
6.  As a terminating condition number of      
generation is chosen to be 100. 
 
 
4. COMPARATIVE STUDY OF GA BASED 
SOLUTION WITH CONVENTIONAL 
SOLUTION 
To evaluate the effectiveness of the proposed GA 
based OPF problem solution examples from [5] are 
taken as reference, the performance of the GA is 
examined under different conditions 
 
1. Economic dispatch neglecting losses and    
generator limits (Example 7.4).  
2. Economic dispatch neglecting losses and including 
generator limits (Example 7.6). 
3.  Economic dispatch including losses and 
generator limits (Example 7.7). 
Comparative results are given in the following 
tables: (Table 1, Table 2, and Table 3) 
 
Table 1:  Comparison of classical and genetic based 
optimal dispatch of generation problem neglecting 
losses and generator limits. 
 
 Classical Genetic 
P1(MW) 400.0000 398.4313 
P2(MW) 250.0000 250.9803 
P3(MW) 150.0000 150.5882 
Cost ($/h) 6682.20 6682.51 
Time(sec) 50.765 0.9677 
 
 
Table 2:  Comparison of classical and genetic based 
optimal dispatch of generation problem neglecting 
losses and including generator limits. 
 
 Classical Genetic 
P1(MW) 450.0000 447.3529 
P2(MW) 325.0000 325.0000 
P3(MW) 200.0000 202.6470 
Cost ($/h) 8236.25 8237.66 
Time(sec) 105.765 1.0103 
 
Table 3:  Comparison of classical and genetic based 
optimal dispatch of generation problem including 
losses and generator limits. 
 
 Classical Genetic 
P1(MW) 35.0907 31.1764 
P2(MW) 64.1317 67.0588 
P3(MW) 52.4767 50.0000 
Cost ($/h) 1592.65 1566.45 
Time(sec) 250.765 1.723 
 
From the tables it is evident that GA based solutions 
are faster than the traditional derivative based 
methods and the computational time differences are 
much more prominent as the constraints are 
increased. 
 
5. CONCLUSION 
A new approach for solving the challenging optimal 
load flow problem is presented. The model for 
solving the mentioned problem is formulated and 
solved at various conditions. The results are 
compared with the results obtained using traditional 
methods. The results are quite similar except that GA 
is much faster than the traditional ones. This 
indicates that the GA approach is quite promising 
and deserves serious attention because of its 
probabilistic transition rules, not deterministic ones.   
References 
[1]  A. Demirören and H.L. Zeynelgil, “The 
Investigation  of Environmental/Economic 
Dispatch using a Genetic Algorithm”, proc. of 
Modelling, Identification, and Control, MIC-
2003. 
[2] R.C. Burchett, H. H. Happ, and D. R. 
Vierath,  “Quadratically Convergent Optimal 
Power Flow,” IEEE Transactions PAS-103, no. 
10,  pp. 3267–3275, Nov. 1984. 
[3] R.C. Burchett, H. H. Happ, and K. A. 
Wirgao, “Large Scale Optimal Power Flow” 
IEEE Transactions PAS-103, no. 10  pp. 3722–
3731, Oct. 1982. 
478
International Conference on Electronics, Computer and Communication (ICECC 2008) 
University of Rajshahi, Bangladesh 
 
ISBN 984-300-002131-3 
 
[4] J. F. Dopaz, O. A. Klitin, G. W. Stagg, and 
M. Watson “An optimization technique for real 
and reactive power allocation” proc. of  IEEE, 
Vol. 55, no. 11, pp1877-1885, 1967. 
[5] W. D. Stevenson, Jr, “Elements of Power 
System Analysis”, McGraw-Hill Book 
Company, 2002. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
[6] Hadi Saadat, “Power System Analysis”, Tata 
McGraw-Hill Publishing Company Limited, 
2002. 
[7] David E. Goldberg, “Genetic Algorithms in 
Search, Optimization and Machine Learning”, 
Pearson Education, 2002. 
[8] S.Rajasekharan, S. A. Vijayalekshmi Pai,              
“Neural Networks, Fuzzy Logic & Genetic 
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A genetic algorithm based approach for solving optimal power flow problem

Volume 32, N0. 1 & 2 January & April, pp. 1-2

Nazrul Islam, Muhammad Ziaulhaq Mamun, Abdullah Al-Aabed

Management of Innovative Activities of Small Entrepreneurial Manufacturing Workshops in Bangladesh

Nazrul Islam, Gour Chandra Saha

Job satisfaction of bank officers in Bangladesh

Umme Habiba, Salma Chowdhury

Use of electronic resources and its impact: a study of Dhaka University Library users

M. G. Rabbani, M. R. I. Sheikh, S. M. A. Razzak

Improvement of power system dynamics using superconducting magnetic energy storage unit

Nazrul Islam, G. M. Shafayet Ullah

Factors affecting consumers’ preferences on fast food items in Bangladesh

Eastern University

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