Optimization Electrical Circuit's Parameter Using Genetic Algorithms (GAs).
Optimization Electrical Circuit’s
Parameter Using Genetic Algorithms (GAs)
PANG LU SHIN
This report is submitted in partial fulfillment of requirements for the award of
Bachelor of Electronic Engineering (Computer Engineering) With Honours
Faculty of Electronic and Computer Engineering
Universiti Teknikal Malaysia Melaka
April 2009
UNIVERSTI TEKNIKAL MALAYSIA MELAKA
FAKULTI KEJURUTERAAN ELEKTRONIK DAN KEJURUTERAAN KOMPUTER
BORANG PENGESAHAN STATUS LAPORAN
PROJEK SARJANA MUDA II
Tajuk Projek
:
Optimization Electrical Circuit’s Parameter Using Genetic
Algorithms (GAs)
Sesi
Pengajian
:
2008/2009
PANG LU SHIN
Saya …………………………………………………..……………………………………………..
(HURUF BESAR)
mengaku membenarkan Laporan Projek Sarjana Muda ini disimpan di Perpustakaan dengan syaratsyarat kegunaan seperti berikut:
1.
Laporan adalah hakmilik Universiti Teknikal Malaysia Melaka.
2.
Perpustakaan dibenarkan membuat salinan untuk tujuan pengajian sahaja.
3.
Perpustakaan dibenarkan membuat salinan laporan ini sebagai bahan pertukaran antara institusi
pengajian tinggi.
4.
Sila tandakan (
):
SULIT*
(Mengandungi maklumat yang berdarjah keselamatan atau
kepentingan Malaysia seperti yang termaktub di dalam AKTA
RAHSIA RASMI 1972)
TERHAD*
(Mengandungi maklumat terhad yang telah ditentukan oleh
organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
Disahkan oleh:
__________________________
(TANDATANGAN PENULIS)
___________________________________
(COP DAN TANDATANGAN PENYELIA)
Alamat Tetap: 108-A, Jalan Pee Kang Hai,
Kampung Abdullah,
85000 Segamat,
Johor.
Tarikh: ………………………..
Tarikh: ………………………..
“I hereby declare that this report is the result of my own work except for quotes as
sited in the references.”
Signature
: ……………………………………………
Author
: Pang Lu Shin
Date
: ……………………………………………
iii
“I hereby declare that this report is the result of my own work except for quotes as
cited in the references.”
Signature
: ………………………………………
Author
: Pang Lu Shin
Date
: ………………………………………
iv
“I hereby declare that I have read this report and in my opinion this report is
sufficient in terms of the scope and quality for the award of Bachelor of Electronic
Engineering (Computer Engineering) With Honours.”
Signature
: ………………………………………
Supervisor’s Name
: Mdm. Wong Yan Chiew
Date
: ………………………………………
v
To my beloved family and friends
vi
ACKNOWLEDGEMENT
I would like to express my gratitude to all those who gave me the possibility
to complete this project. First of all, I would like to take this opportunity to express
my deepest gratitude to my supervisor of this project, Mdm. Wong Yan Chiew who
has relentlessly and tirelessly assisted me in completing this project. She has give me
support and insight in doing this project and has patiently listened and guided. My
utmost thanks also go to my family who has given me support throughout my
academic years. It is to advantage that I have received help and support from friends
and staff in the faculty labs. I want to thank them for all their help, support, interest
and valuable hints. It is of my greatest thanks and joy that I have met with this people.
Thank you.
vii
ABSTRACT
Genetic algorithms (GAs) for automation design of analog circuits are
proposed in this project. It is used to solve complex and considerable constrained
combinatorial optimization problems that one generally encounters in designing
electrical circuits. For the large-scale circuits, the key problems of circuit design are
its large amount of calculating operations and the chance of falling into local optimal
point. During the optimization, it is often needed to adjust several circuit elements
simultaneously. Those parameters will have the significant effect to the circuit
performance. The search space is therefore very large, and genetic algorithms have
been used with success to optimize such circuit parameters. In this project, the
artificial intelligence GAs is proposed to order to generate evolutionary analog
circuit designs. The results show that the appropriate combination of circuit
components e.g. inductor and resistor are selected by the proposed GAs is able to
meet the desired design specification of the circuit. Finally, the simulation result is
compared and validated with the manual hand calculation result.
viii
ABSTRAK
Algorithma genetik (GAs) untuk automasi rekabentuk litar analog telah
dicadangkan dalam projek ini. Ia digunakan untuk menyelesaikan masalah yang
kompleks dan melibatkan pertimbangan sekatan cabang optimasi yang selalunya
berlaku dalam isu mereka bentuk litar elektrik. Untuk litar yang mempunyai skala
yang besar, masalah yang paling ketara dalam isu mereka bentuk litar adalah operasi
pengiraan yang banyak dan peluang untuk mencapai local titik optimum. Semasa
optimasi dijalankan, ia sering diperlukan untuk mengubah beberapa element litar
dengan serentak. Parameter tersebut mempunyai kesan yang bererti kepada preatasi
litar. Oleh sebab itu, pencarian ruang adalah sangat luas dan algorithma genetik telah
digunakan dengan berjayanya untuk mengoptimasikan parameter litar tersebut.
Dalam projek ini, kecerdasan buatan GAs telah dicadangkan untuk menghasilkan
perkembangan reka bentuk litar analog. Keputusan telah menunjukkan combinasi
komponen litar yang sesuai contohnya induktor dan perintang telah dipilih oleh GAs
adalah berupaya memenuhi spesifikasi reka bentuk litar yang telah disediakan.
Akhirnya, keputusan simulasi telah dibanding dan dibuktikan dengan keputusan
pengiraan tangan.
ix
CONTENTS
CHAPTER
I
SUBJECT
PAGE
TITLE
i
REPORT STATUS VERIFICATION FORM
ii
DECLARATION
iii
SUPERVISOR VERIFICATION
iv
DEDICATION
v
ACKNOWLEDGEMENTS
vi
ABSTRACT
vii
ABSTRAK
viii
CONTENTS
ix
LIST OF TABLES
xii
LIST OF FIGURES
xiii
LIST OF SHORT FORM
xv
LIST OF APPENDIX
xvi
INTRODUCTION
1
1.1
Chapter Overview
1
1.2
Introduction of Project
1
1.3
Objectives
2
1.4
Scope of Project
3
1.5
Problem Statement
3
1.6
Methodology
3
1.7
Thesis Outline
4
1.8
Conclusion
5
x
II
LITERATURE REVIEW
6
2.1
Chapter Overview
6
2.2
Genetic Algorithms
6
2.3
Operations of Genetic Algorithms
7
2.3.1 Chromosome
8
2.3.2 Reproduction
9
2.3.3 Crossover
9
2.3.4 Mutation
10
2.3.5 Fitness Function
10
2.4
Pros and Cons of Genetic Algorithms
11
2.5
Others related optimization techniques
12
2.5.1 Ant Colony Optimization
12
2.5.2 Evolutionary programming (EP)
12
2.5.3 Simulated annealing (SA)
13
2.5.4 Hill-climbing
13
2.5.5 Tabu Search (TS)
14
2.5.6 Harmonic Search (HS)
14
2.5.7 External optimization (EO)
15
2.6
Simulation environment using genetic
algorithms
2.7
2.8
2.9
III
15
2.6.1 Visual Basic
15
2.6.2 MATrix LABoratory (MATLAB)
16
2.6.3 Genetic Algorithms Toolbox
18
Graphical User Interface (GUI)
20
2.7.1
Matlab GUI compare with others
20
2.7.2
Disadvantages of GUI
20
Optimization of Electrical Circuits
21
2.8.1
Rectifier
21
2.8.2
Operational Amplifier
22
Conclusion
METHODOLOGY
23
24
xi
IV
V
3.1
Chapter Overview
24
3.2
Project Flow
25
3.3
Project Methodology
26
3.4
Program Flow
27
3.5
Program Development by using MATLAB
28
3.6
Conclusion
31
RESULT AND DISCUSSION
32
4.1
Chapter Overview
32
4.2
Optimization of Half-wave Rectifier
32
4.3
Simulation Result of Half-wave Rectifier
35
4.4
Half-wave Rectifier with Additional Element
42
4.5
Band Pass Filter Using Op-Amp
49
4.6
Discussion
53
4.7
Conclusion
54
CONCLUSION AND SUGGESTION
55
4.1
Chapter Overview
55
4.2
Conclusion
55
4.3
Recommendation for Future Work
56
REFERENCES
57
APPENDIX
59
xii
LIST OF TABLES
NO
TITLE
PAGE
2.1
Standard algorithm options in Genetic Algorithm and
19
Direct Search Toolbox
4.1
The calculated maximum power for half-wave rectifier
41
4.2
The calculated maximum power for half-wave rectifier
48
with additional capacitor
4.3
The comparison between the simulation result and the
manual hand calculation result
52
xiii
LIST OF FIGURES
NO
2.1
TITLE
Standard procedures of GA operations from one generation
PAGE
8
to the next.
2.2
Single point crossover
9
2.3
Example of mutation process
10
3.1
Flow chart for project planning
26
3.2
Monitoring program flow chart
28
3.3
Steps of launch the gatool from the MATLAB Start menu
29
3.4
Genetic Algorithm Tool is a graphical user interface in
30
MATLAB
4.1
Passive filter circuit topology for half-wave rectifier
34
4.2
GUI for generating the half-wave rectifier variables.
36
4.3
Fitness value versus generation plot for the half-wave
37
rectifier
4.4
The plot of best individual, distance, score diversity and
38
selection for the half-wave rectifier
4.5
The message box to export the GAs result to the workspace
38
4.6
The garesults exported to the workspace
39
4.7
The gaproblem exported to the workspace
39
4.8
The gaoptions exported to the workspace
40
4.9
GUI for generating the half-wave rectifier with additional of
42
capacitor.
4.10
Fitness value versus generation plot for the half-wave
rectifier with additional of capacitor
43
xiv
4.11
The plot of best individual and expectation for the half-wave
44
rectifier with additional of capacitor connected in series
4.12
The plot of range and stopping criterion for the half-wave
45
rectifier with additional of capacitor connected in series
4.13
The garesults exported to the workspace
45
4.14
The gaproblem exported to the workspace
46
4.15
The gaoptions exported to the workspace
47
4.16
Band pass Circuit using active element (Op-amp)
49
4.17
GUI for optimizing band pass filter to get the wider
51
bandwidth and higher gain
xv
LIST OF SYMBOL
AC
-
Alternating Current
ACO
-
Ant colony optimization algorithm
DC
-
Direct Current
EO
-
External optimization
EP
-
Evolutionary programming
GAs
-
Genetic Algorithms
GUI
-
Graphical User Interface
HS
-
Harmonic Search
MATLAB
-
Matrix Labroratory
SA
-
Simulated annealing
TS
-
Tabu Search
VB
-
Visual Basic
xvi
LIST OF APPENDIX
NO
TITLE
PAGE
A
Matlab Source Code
59
B
Technical Paper
63
C
Genetic Algorithms Guideline
70
CHAPTER I
INTRODUCTION
1.1
Chapter Overview
The introduction of the project, objectives and the scope of project is briefly
discussed in this chapter. After that, the implementation of the project is introduced
by a methodology and lastly the contents of Chapter 1 until Chapter 5 is highlighted
in thesis outline.
1.2
Introduction of Project
Nowadays, circuit design is the basis of implementing any electronic system,
and its object is to make the designed system having optimal performance and
reliability. Therefore, the important of the optimization of the circuit is to find the
maximum or minimum value of an objective with various limitations. There are
some difficulties in conducting the optimization for electrical circuit such as taking
considerable amount of the trial and error testing to determine the circuit’s parameter
value.
Conventional design approaches produce circuits in which the designed
component values are assumed to be ideal with unrestricted values. It may be
required to repeat the design with a more stringent specification or to use a more
2
closely spaced set of acceptable values. However, a better circuit performance may
potentially be achieved than that obtained by conventional method if other
combinations of acceptable values are considered. Eventually, the difficulty is that
the search space of all feasible combinations is too huge. In addition, circuit
specifications are usually identified in terms of acceptability regions with upper and
lower bounds rather than specific values. It is useful to define an estimation of the
feasible region as the set of parameter values for which the design specifications are
satisfied when designing a circuit according to certain specifications.
Hence, the genetic algorithm is proposed to define appropriate parameters
values to meet the desired circuit performance with its ability of parallel searching
through the entire solution space. In GAs implementation, the estimation of the
feasible region plays an important role in the determination of the solution space
volume to be searched. A search over the feasible region estimate will greatly
decrease the time needed to cover all possible solutions of a problem using GAs. The
proposed GAs is realized by computer program, which can provide a simplified
design procedure. Furthermore, the advantage of GAs program is the capable to find
a set of optimal solution for the complex and complicated electrical circuit instead of
reduce the conventional design time of the circuit.
However, there is some issue that genetic algorithms need to take in
consideration such as the fitness function has been created is compatible so that
higher fitness is achieved and result in a better solution for the given circuit.
1.3
Objectives
The main objective of the project is to build an Artificial Intelligent (AI)
algorithm which can engage on behaviors that humans intelligent in electronic
circuits. Another purpose of this project is to develop an algorithm for finding the
optimization electrical circuit’s parameters. Besides that, the characteristics of
various kinds of electrical circuits can be defined in the process to determine the
optimization electrical circuit’s parameters.
3
1.4
Scope of Project
The scope of this project is focusing on utilization of genetic algorithms in
solving various circuit optimizations with pre-defined constraints in electrical circuit.
The project will be implemented by an attractive and easy way for accessing and
learning tool which is Matrix Laboratory (MATLAB). This project is managing the
optimization, monitoring performance, and defining stopping criteria for the
electrical circuit. Several types of electrical circuit will be designed by using
MATLAB M-file. It will be implemented to let the user quickly define their problem
and set a variety of algorithm options to fine-tune the optimization.
1.5
Problem Statement
Genetic Algorithms were developed over twenty-five years ago, but there is
not so much research and experimental work has been down to ascertain their
capabilities in solving complex and considerable constrained combinatorial
optimization problems that one generally encounters in designing electrical circuits.
Hence, an optimization electrical circuit’s parameter using genetic algorithms was
proposed to speed up or eliminate a considerable amount of the trial and error testing
being done by designers today. This project has emphasize genetic algorithms may
be a better alternative for global optimization tools for these electrical circuits.
1.6
Methodology
The methodology of the project is starting with choosing the simulation’s
environment used in developing an optimization electrical circuit’s parameter using
genetic algorithms. As a result, MATLAB has been chosen to develop the algorithms
environment. The advance tools provides in the MATLAB able to support and
provide comprehensive information in implementing the genetic operators such as
crossover, mutation and selection.
4
The next step is to characterize and define the electrical circuits which need
to optimize. After that, a program is created where the defined fitness function
(inputs of the function which want to minimize maximize or optimize) is used to
generate a set of variables and output the desired circuit performance. The program is
written in MATLAB's editor because of its advance tools and options provided in the
GAs toolbox and easiest to use compare with others.
After developed the algorithms, then it will apply to a graphical user interface
(GUI) of GA tools in MATLAB which has provide an approach to let the user
quickly define their problem and set a variety of algorithm options to fine-tune the
optimization.
1.7
Thesis Outline
This thesis is a document that delivers the idea generated, concepts applied,
activities done in optimization electrical circuit’s parameter using genetic algorithms
and the final year project produced. It consists of five chapters which are
Introduction, Literature Review, Methodology, Result and Discussion, Conclusion
and Recommendation.
Chapter 1 is delivering the introduction of the project. It consists of objective,
problem statement, scope of project, methodology and thesis outline of this project.
Chapter 2 is discussing the literature review of this project. The features and
behavior of genetic algorithms are studied.
Chapter 3 is briefly described the project flow and the functional of genetic
algorithms. It also covered the methods used in this project and the reason of
choosing these methods.
Chapter 4 is deals with the analysis of result at the final stage which is
complete designed the interface of optimization electrical circuit parameter by using
5
genetic algorithms. The result and discussion on writing an algorithm in MATLAB
will be presented in this chapter.
Chapter 5 is described the conclusion and result of the project at the final
stage. The recommendation and future development of this project is discussed in
order to upgrade and enhance the optimization electrical circuit’s parameter using
genetic algorithms (GAs).
1.8
Conclusion
As a conclusion, the details of introduction of the project in this chapter are
briefly described. Besides that, the objective of this project has been classified. The
problem statement has defined the optimization problem facing by the user. In
additional, the scope of the project is clearly defined. Finally, there is a brief
description of literature review which will be presented in detail in Chapter II.
CHAPTER II
LITERATURE REVIEW
2.1
Chapter Overview
Genetic algorithms and its behaviors in finding the optimization electrical
circuit’s parameters are explained in this chapter. Besides, some theories and
concepts of genetic algorithms are presented in this chapter. Others related
optimization techniques and the simulation environment of GAs will also be
discussed in this chapter as well.
2.2
Genetic Algorithms
The GAs is a search mechanism based on the principle of natural selection
and population genetics. On the others hands, it can also defined as adaptive
algorithms for finding the global optimum solution for an optimization problem.
The primary framework of GAs was first proposed by Dr. J. Holland in 1975 [1].
The continuing performance improvement of computational systems has made them
attractive for some types of optimization [2]. In particular, genetic algorithms work
very well on mixed (continuous and discrete), combinatorial problems. A solution
must be represented to the problem as a genome (or chromosome) in order to use a
genetic algorithm. The genetic algorithm then creates a population of solutions and
7
applies genetic operators such as mutation and crossover to evolve the solutions in
order to find the optimal solution.
2.3
Operation of Genetic Algorithms
A simple GAs is described in this section. While more sophisticated forms of
the GAs exist, they are all extensions of this basic algorithm. The simple GAs
provides excellent performance for a wide range of problems [7].
It begins with the creation of an initial random population of individuals. The
size of the population depends on the nature of problem, but typically contains
several hundred or thousands of possible solutions [8]. Values of several hundred are
commonly used. Traditionally, the population is generated randomly, covering the
entire range of possible solutions or the search space. The next step is to evaluate the
fitness of each individual according to the predefined criteria.
After the evaluation, the individuals are ranked according to their fitness.
Individuals are selected for mating based on fitness. Fitter individuals have a higher
probability of mating and passing on genetic information to subsequent generations
while less fit individuals have a non-zero probability of mating to preserve diversity
[11]. Mating is simulated by applying the crossover operation to the chromosomes
of two individuals selected based their fitness.
Mutation is simulated by randomly changing a few bits in the chromosome
of the offspring [12]. Mutation provides a mechanism for exploring new regions of
the solution space and prevents premature convergence to local minima. Finally, the
fitness of the new generation is evaluated and the process is repeated for a specified
number of generations or until a desired fitness is attained.
Parameter Using Genetic Algorithms (GAs)
PANG LU SHIN
This report is submitted in partial fulfillment of requirements for the award of
Bachelor of Electronic Engineering (Computer Engineering) With Honours
Faculty of Electronic and Computer Engineering
Universiti Teknikal Malaysia Melaka
April 2009
UNIVERSTI TEKNIKAL MALAYSIA MELAKA
FAKULTI KEJURUTERAAN ELEKTRONIK DAN KEJURUTERAAN KOMPUTER
BORANG PENGESAHAN STATUS LAPORAN
PROJEK SARJANA MUDA II
Tajuk Projek
:
Optimization Electrical Circuit’s Parameter Using Genetic
Algorithms (GAs)
Sesi
Pengajian
:
2008/2009
PANG LU SHIN
Saya …………………………………………………..……………………………………………..
(HURUF BESAR)
mengaku membenarkan Laporan Projek Sarjana Muda ini disimpan di Perpustakaan dengan syaratsyarat kegunaan seperti berikut:
1.
Laporan adalah hakmilik Universiti Teknikal Malaysia Melaka.
2.
Perpustakaan dibenarkan membuat salinan untuk tujuan pengajian sahaja.
3.
Perpustakaan dibenarkan membuat salinan laporan ini sebagai bahan pertukaran antara institusi
pengajian tinggi.
4.
Sila tandakan (
):
SULIT*
(Mengandungi maklumat yang berdarjah keselamatan atau
kepentingan Malaysia seperti yang termaktub di dalam AKTA
RAHSIA RASMI 1972)
TERHAD*
(Mengandungi maklumat terhad yang telah ditentukan oleh
organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
Disahkan oleh:
__________________________
(TANDATANGAN PENULIS)
___________________________________
(COP DAN TANDATANGAN PENYELIA)
Alamat Tetap: 108-A, Jalan Pee Kang Hai,
Kampung Abdullah,
85000 Segamat,
Johor.
Tarikh: ………………………..
Tarikh: ………………………..
“I hereby declare that this report is the result of my own work except for quotes as
sited in the references.”
Signature
: ……………………………………………
Author
: Pang Lu Shin
Date
: ……………………………………………
iii
“I hereby declare that this report is the result of my own work except for quotes as
cited in the references.”
Signature
: ………………………………………
Author
: Pang Lu Shin
Date
: ………………………………………
iv
“I hereby declare that I have read this report and in my opinion this report is
sufficient in terms of the scope and quality for the award of Bachelor of Electronic
Engineering (Computer Engineering) With Honours.”
Signature
: ………………………………………
Supervisor’s Name
: Mdm. Wong Yan Chiew
Date
: ………………………………………
v
To my beloved family and friends
vi
ACKNOWLEDGEMENT
I would like to express my gratitude to all those who gave me the possibility
to complete this project. First of all, I would like to take this opportunity to express
my deepest gratitude to my supervisor of this project, Mdm. Wong Yan Chiew who
has relentlessly and tirelessly assisted me in completing this project. She has give me
support and insight in doing this project and has patiently listened and guided. My
utmost thanks also go to my family who has given me support throughout my
academic years. It is to advantage that I have received help and support from friends
and staff in the faculty labs. I want to thank them for all their help, support, interest
and valuable hints. It is of my greatest thanks and joy that I have met with this people.
Thank you.
vii
ABSTRACT
Genetic algorithms (GAs) for automation design of analog circuits are
proposed in this project. It is used to solve complex and considerable constrained
combinatorial optimization problems that one generally encounters in designing
electrical circuits. For the large-scale circuits, the key problems of circuit design are
its large amount of calculating operations and the chance of falling into local optimal
point. During the optimization, it is often needed to adjust several circuit elements
simultaneously. Those parameters will have the significant effect to the circuit
performance. The search space is therefore very large, and genetic algorithms have
been used with success to optimize such circuit parameters. In this project, the
artificial intelligence GAs is proposed to order to generate evolutionary analog
circuit designs. The results show that the appropriate combination of circuit
components e.g. inductor and resistor are selected by the proposed GAs is able to
meet the desired design specification of the circuit. Finally, the simulation result is
compared and validated with the manual hand calculation result.
viii
ABSTRAK
Algorithma genetik (GAs) untuk automasi rekabentuk litar analog telah
dicadangkan dalam projek ini. Ia digunakan untuk menyelesaikan masalah yang
kompleks dan melibatkan pertimbangan sekatan cabang optimasi yang selalunya
berlaku dalam isu mereka bentuk litar elektrik. Untuk litar yang mempunyai skala
yang besar, masalah yang paling ketara dalam isu mereka bentuk litar adalah operasi
pengiraan yang banyak dan peluang untuk mencapai local titik optimum. Semasa
optimasi dijalankan, ia sering diperlukan untuk mengubah beberapa element litar
dengan serentak. Parameter tersebut mempunyai kesan yang bererti kepada preatasi
litar. Oleh sebab itu, pencarian ruang adalah sangat luas dan algorithma genetik telah
digunakan dengan berjayanya untuk mengoptimasikan parameter litar tersebut.
Dalam projek ini, kecerdasan buatan GAs telah dicadangkan untuk menghasilkan
perkembangan reka bentuk litar analog. Keputusan telah menunjukkan combinasi
komponen litar yang sesuai contohnya induktor dan perintang telah dipilih oleh GAs
adalah berupaya memenuhi spesifikasi reka bentuk litar yang telah disediakan.
Akhirnya, keputusan simulasi telah dibanding dan dibuktikan dengan keputusan
pengiraan tangan.
ix
CONTENTS
CHAPTER
I
SUBJECT
PAGE
TITLE
i
REPORT STATUS VERIFICATION FORM
ii
DECLARATION
iii
SUPERVISOR VERIFICATION
iv
DEDICATION
v
ACKNOWLEDGEMENTS
vi
ABSTRACT
vii
ABSTRAK
viii
CONTENTS
ix
LIST OF TABLES
xii
LIST OF FIGURES
xiii
LIST OF SHORT FORM
xv
LIST OF APPENDIX
xvi
INTRODUCTION
1
1.1
Chapter Overview
1
1.2
Introduction of Project
1
1.3
Objectives
2
1.4
Scope of Project
3
1.5
Problem Statement
3
1.6
Methodology
3
1.7
Thesis Outline
4
1.8
Conclusion
5
x
II
LITERATURE REVIEW
6
2.1
Chapter Overview
6
2.2
Genetic Algorithms
6
2.3
Operations of Genetic Algorithms
7
2.3.1 Chromosome
8
2.3.2 Reproduction
9
2.3.3 Crossover
9
2.3.4 Mutation
10
2.3.5 Fitness Function
10
2.4
Pros and Cons of Genetic Algorithms
11
2.5
Others related optimization techniques
12
2.5.1 Ant Colony Optimization
12
2.5.2 Evolutionary programming (EP)
12
2.5.3 Simulated annealing (SA)
13
2.5.4 Hill-climbing
13
2.5.5 Tabu Search (TS)
14
2.5.6 Harmonic Search (HS)
14
2.5.7 External optimization (EO)
15
2.6
Simulation environment using genetic
algorithms
2.7
2.8
2.9
III
15
2.6.1 Visual Basic
15
2.6.2 MATrix LABoratory (MATLAB)
16
2.6.3 Genetic Algorithms Toolbox
18
Graphical User Interface (GUI)
20
2.7.1
Matlab GUI compare with others
20
2.7.2
Disadvantages of GUI
20
Optimization of Electrical Circuits
21
2.8.1
Rectifier
21
2.8.2
Operational Amplifier
22
Conclusion
METHODOLOGY
23
24
xi
IV
V
3.1
Chapter Overview
24
3.2
Project Flow
25
3.3
Project Methodology
26
3.4
Program Flow
27
3.5
Program Development by using MATLAB
28
3.6
Conclusion
31
RESULT AND DISCUSSION
32
4.1
Chapter Overview
32
4.2
Optimization of Half-wave Rectifier
32
4.3
Simulation Result of Half-wave Rectifier
35
4.4
Half-wave Rectifier with Additional Element
42
4.5
Band Pass Filter Using Op-Amp
49
4.6
Discussion
53
4.7
Conclusion
54
CONCLUSION AND SUGGESTION
55
4.1
Chapter Overview
55
4.2
Conclusion
55
4.3
Recommendation for Future Work
56
REFERENCES
57
APPENDIX
59
xii
LIST OF TABLES
NO
TITLE
PAGE
2.1
Standard algorithm options in Genetic Algorithm and
19
Direct Search Toolbox
4.1
The calculated maximum power for half-wave rectifier
41
4.2
The calculated maximum power for half-wave rectifier
48
with additional capacitor
4.3
The comparison between the simulation result and the
manual hand calculation result
52
xiii
LIST OF FIGURES
NO
2.1
TITLE
Standard procedures of GA operations from one generation
PAGE
8
to the next.
2.2
Single point crossover
9
2.3
Example of mutation process
10
3.1
Flow chart for project planning
26
3.2
Monitoring program flow chart
28
3.3
Steps of launch the gatool from the MATLAB Start menu
29
3.4
Genetic Algorithm Tool is a graphical user interface in
30
MATLAB
4.1
Passive filter circuit topology for half-wave rectifier
34
4.2
GUI for generating the half-wave rectifier variables.
36
4.3
Fitness value versus generation plot for the half-wave
37
rectifier
4.4
The plot of best individual, distance, score diversity and
38
selection for the half-wave rectifier
4.5
The message box to export the GAs result to the workspace
38
4.6
The garesults exported to the workspace
39
4.7
The gaproblem exported to the workspace
39
4.8
The gaoptions exported to the workspace
40
4.9
GUI for generating the half-wave rectifier with additional of
42
capacitor.
4.10
Fitness value versus generation plot for the half-wave
rectifier with additional of capacitor
43
xiv
4.11
The plot of best individual and expectation for the half-wave
44
rectifier with additional of capacitor connected in series
4.12
The plot of range and stopping criterion for the half-wave
45
rectifier with additional of capacitor connected in series
4.13
The garesults exported to the workspace
45
4.14
The gaproblem exported to the workspace
46
4.15
The gaoptions exported to the workspace
47
4.16
Band pass Circuit using active element (Op-amp)
49
4.17
GUI for optimizing band pass filter to get the wider
51
bandwidth and higher gain
xv
LIST OF SYMBOL
AC
-
Alternating Current
ACO
-
Ant colony optimization algorithm
DC
-
Direct Current
EO
-
External optimization
EP
-
Evolutionary programming
GAs
-
Genetic Algorithms
GUI
-
Graphical User Interface
HS
-
Harmonic Search
MATLAB
-
Matrix Labroratory
SA
-
Simulated annealing
TS
-
Tabu Search
VB
-
Visual Basic
xvi
LIST OF APPENDIX
NO
TITLE
PAGE
A
Matlab Source Code
59
B
Technical Paper
63
C
Genetic Algorithms Guideline
70
CHAPTER I
INTRODUCTION
1.1
Chapter Overview
The introduction of the project, objectives and the scope of project is briefly
discussed in this chapter. After that, the implementation of the project is introduced
by a methodology and lastly the contents of Chapter 1 until Chapter 5 is highlighted
in thesis outline.
1.2
Introduction of Project
Nowadays, circuit design is the basis of implementing any electronic system,
and its object is to make the designed system having optimal performance and
reliability. Therefore, the important of the optimization of the circuit is to find the
maximum or minimum value of an objective with various limitations. There are
some difficulties in conducting the optimization for electrical circuit such as taking
considerable amount of the trial and error testing to determine the circuit’s parameter
value.
Conventional design approaches produce circuits in which the designed
component values are assumed to be ideal with unrestricted values. It may be
required to repeat the design with a more stringent specification or to use a more
2
closely spaced set of acceptable values. However, a better circuit performance may
potentially be achieved than that obtained by conventional method if other
combinations of acceptable values are considered. Eventually, the difficulty is that
the search space of all feasible combinations is too huge. In addition, circuit
specifications are usually identified in terms of acceptability regions with upper and
lower bounds rather than specific values. It is useful to define an estimation of the
feasible region as the set of parameter values for which the design specifications are
satisfied when designing a circuit according to certain specifications.
Hence, the genetic algorithm is proposed to define appropriate parameters
values to meet the desired circuit performance with its ability of parallel searching
through the entire solution space. In GAs implementation, the estimation of the
feasible region plays an important role in the determination of the solution space
volume to be searched. A search over the feasible region estimate will greatly
decrease the time needed to cover all possible solutions of a problem using GAs. The
proposed GAs is realized by computer program, which can provide a simplified
design procedure. Furthermore, the advantage of GAs program is the capable to find
a set of optimal solution for the complex and complicated electrical circuit instead of
reduce the conventional design time of the circuit.
However, there is some issue that genetic algorithms need to take in
consideration such as the fitness function has been created is compatible so that
higher fitness is achieved and result in a better solution for the given circuit.
1.3
Objectives
The main objective of the project is to build an Artificial Intelligent (AI)
algorithm which can engage on behaviors that humans intelligent in electronic
circuits. Another purpose of this project is to develop an algorithm for finding the
optimization electrical circuit’s parameters. Besides that, the characteristics of
various kinds of electrical circuits can be defined in the process to determine the
optimization electrical circuit’s parameters.
3
1.4
Scope of Project
The scope of this project is focusing on utilization of genetic algorithms in
solving various circuit optimizations with pre-defined constraints in electrical circuit.
The project will be implemented by an attractive and easy way for accessing and
learning tool which is Matrix Laboratory (MATLAB). This project is managing the
optimization, monitoring performance, and defining stopping criteria for the
electrical circuit. Several types of electrical circuit will be designed by using
MATLAB M-file. It will be implemented to let the user quickly define their problem
and set a variety of algorithm options to fine-tune the optimization.
1.5
Problem Statement
Genetic Algorithms were developed over twenty-five years ago, but there is
not so much research and experimental work has been down to ascertain their
capabilities in solving complex and considerable constrained combinatorial
optimization problems that one generally encounters in designing electrical circuits.
Hence, an optimization electrical circuit’s parameter using genetic algorithms was
proposed to speed up or eliminate a considerable amount of the trial and error testing
being done by designers today. This project has emphasize genetic algorithms may
be a better alternative for global optimization tools for these electrical circuits.
1.6
Methodology
The methodology of the project is starting with choosing the simulation’s
environment used in developing an optimization electrical circuit’s parameter using
genetic algorithms. As a result, MATLAB has been chosen to develop the algorithms
environment. The advance tools provides in the MATLAB able to support and
provide comprehensive information in implementing the genetic operators such as
crossover, mutation and selection.
4
The next step is to characterize and define the electrical circuits which need
to optimize. After that, a program is created where the defined fitness function
(inputs of the function which want to minimize maximize or optimize) is used to
generate a set of variables and output the desired circuit performance. The program is
written in MATLAB's editor because of its advance tools and options provided in the
GAs toolbox and easiest to use compare with others.
After developed the algorithms, then it will apply to a graphical user interface
(GUI) of GA tools in MATLAB which has provide an approach to let the user
quickly define their problem and set a variety of algorithm options to fine-tune the
optimization.
1.7
Thesis Outline
This thesis is a document that delivers the idea generated, concepts applied,
activities done in optimization electrical circuit’s parameter using genetic algorithms
and the final year project produced. It consists of five chapters which are
Introduction, Literature Review, Methodology, Result and Discussion, Conclusion
and Recommendation.
Chapter 1 is delivering the introduction of the project. It consists of objective,
problem statement, scope of project, methodology and thesis outline of this project.
Chapter 2 is discussing the literature review of this project. The features and
behavior of genetic algorithms are studied.
Chapter 3 is briefly described the project flow and the functional of genetic
algorithms. It also covered the methods used in this project and the reason of
choosing these methods.
Chapter 4 is deals with the analysis of result at the final stage which is
complete designed the interface of optimization electrical circuit parameter by using
5
genetic algorithms. The result and discussion on writing an algorithm in MATLAB
will be presented in this chapter.
Chapter 5 is described the conclusion and result of the project at the final
stage. The recommendation and future development of this project is discussed in
order to upgrade and enhance the optimization electrical circuit’s parameter using
genetic algorithms (GAs).
1.8
Conclusion
As a conclusion, the details of introduction of the project in this chapter are
briefly described. Besides that, the objective of this project has been classified. The
problem statement has defined the optimization problem facing by the user. In
additional, the scope of the project is clearly defined. Finally, there is a brief
description of literature review which will be presented in detail in Chapter II.
CHAPTER II
LITERATURE REVIEW
2.1
Chapter Overview
Genetic algorithms and its behaviors in finding the optimization electrical
circuit’s parameters are explained in this chapter. Besides, some theories and
concepts of genetic algorithms are presented in this chapter. Others related
optimization techniques and the simulation environment of GAs will also be
discussed in this chapter as well.
2.2
Genetic Algorithms
The GAs is a search mechanism based on the principle of natural selection
and population genetics. On the others hands, it can also defined as adaptive
algorithms for finding the global optimum solution for an optimization problem.
The primary framework of GAs was first proposed by Dr. J. Holland in 1975 [1].
The continuing performance improvement of computational systems has made them
attractive for some types of optimization [2]. In particular, genetic algorithms work
very well on mixed (continuous and discrete), combinatorial problems. A solution
must be represented to the problem as a genome (or chromosome) in order to use a
genetic algorithm. The genetic algorithm then creates a population of solutions and
7
applies genetic operators such as mutation and crossover to evolve the solutions in
order to find the optimal solution.
2.3
Operation of Genetic Algorithms
A simple GAs is described in this section. While more sophisticated forms of
the GAs exist, they are all extensions of this basic algorithm. The simple GAs
provides excellent performance for a wide range of problems [7].
It begins with the creation of an initial random population of individuals. The
size of the population depends on the nature of problem, but typically contains
several hundred or thousands of possible solutions [8]. Values of several hundred are
commonly used. Traditionally, the population is generated randomly, covering the
entire range of possible solutions or the search space. The next step is to evaluate the
fitness of each individual according to the predefined criteria.
After the evaluation, the individuals are ranked according to their fitness.
Individuals are selected for mating based on fitness. Fitter individuals have a higher
probability of mating and passing on genetic information to subsequent generations
while less fit individuals have a non-zero probability of mating to preserve diversity
[11]. Mating is simulated by applying the crossover operation to the chromosomes
of two individuals selected based their fitness.
Mutation is simulated by randomly changing a few bits in the chromosome
of the offspring [12]. Mutation provides a mechanism for exploring new regions of
the solution space and prevents premature convergence to local minima. Finally, the
fitness of the new generation is evaluated and the process is repeated for a specified
number of generations or until a desired fitness is attained.