Intelligent optimisation techniques: genetic algorithms, tabu search, simulated annealing and neural networks
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
London [u.a.]
Springer
2000
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008144544&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | X, 302 S. zahlr. graph. Darst. |
| ISBN: | 1852330287 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
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| 100 | 1 | |a Pham, Duc Truong |d 1952- |e Verfasser |0 (DE-588)121726029 |4 aut | |
| 245 | 1 | 0 | |a Intelligent optimisation techniques |b genetic algorithms, tabu search, simulated annealing and neural networks |c D. T. Pham and D. Karaboga |
| 264 | 1 | |a London [u.a.] |b Springer |c 2000 | |
| 300 | |a X, 302 S. |b zahlr. graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Ingenieurwissenschaften | |
| 650 | 4 | |a Computer-aided engineering | |
| 650 | 4 | |a Engineering |x Data processing | |
| 650 | 4 | |a Genetic algorithms | |
| 650 | 4 | |a Heuristic programming | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 650 | 4 | |a Simulated annealing (Mathematics) | |
| 650 | 0 | 7 | |a Tabusuche |0 (DE-588)4432514-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Simulated annealing |0 (DE-588)4265091-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Genetischer Algorithmus |0 (DE-588)4265092-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Optimierung |0 (DE-588)4043664-0 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
| _version_ | 1819347188304576512 |
|---|---|
| adam_text | Contents
1
Introduction
.............................................................................................1
1.1
Genetic Algorithms
.....................................................................................1
1.1.1
Background
.......................................................................................1
1.1.2
Representation
..................................................................................2
1.1.3
Creation of Initial Population
...........................................................3
1.1.4
Genetic Operators
.............................................................................3
1.1.5
Control Parameters
...........................................................................7
1.1.6
Fitness Evaluation Function
..............................................................7
1.2
Tabu Search
................................................................................................8
1.2.1
Background
.......................................................................................8
1.2.2
Strategies
..........................................................................................8
1.3
Simulated Annealing
..................................................................................11
1.3.1
Background
.......................................................................................11
1.3.2
Basic Elements
..................................................................................13
1.4
Neural Networks
.........................................................................................15
1.4.1
Basic Unit
.........................................................................................15
1.4.2
Structural Categorisation
..................................................................18
1.4.3
Learning Algorithm Categorisation
..................................................19
1.4.4
Optimisation Algorithms
...................................................................20
1.4.5
Example Neural Networks
................................................................22
1.5
Performance of Different Optimisation Techniques on Benchmark Test
Functions
..................................................................................................27
1.5.1
Genetic Algorithm Used
...................................................................28
1.5.2
Tabu Search Algorithm Used
............................................................30
1.5.3
Simulated Annealing Algorithm Used
..............................................31
1.5.4
Neural Network Used
.......................................................................31
1.5.5
Results
..............................................................................................33
1.6
Performance of Different Optimisation Techniques on Travelling
Salesman Problem
....................................................................................44
1.6.1
Genetic Algorithm Used
...................................................................44
1.6.2
Tabu Search Algorithm Used
............................................................45
1.6.3
Simulated Annealing Algorithm Used
..............................................45
viii Contents
1.6.4
Neural
Network
Used
.......................................................................46
1.6.5
Results
..............................................................................................47
1.7
Summary
.....................................................................................................47
References
.........................................................................................................47
2
Genetic Algorithms
...............................................................................51
2.1
New Models
................................................................................................51
2.1.1
Hybrid Genetic Algorithm
................................................................51
2.1.2
Cross-Breeding in Genetic Optimisation
..........................................62
2.1.3
Genetic Algorithm with the Ability to Increase the Number of
Alternative Solutions
.................................................................................63
2.1.4
Genetic Algorithms with Variable Mutation Rates
...........................69
2.2
Engineering Applications
............................................................................78
2.2.1
Design of Static Fuzzy Logic Controllers
.........................................78
2.2.2
Training Recurrent Neural Networks
................................................97
2.2.3
Adaptive Fuzzy Logic Controller Design
.........................................
Ill
2.2.4
Preliminary Gearbox Design
.............................................................126
2.2.5 Ergonomie
Workplace Layout Design
..............................................131
2.3
Summary
.....................................................................................................140
References
.........................................................................................................141
3
Tabu Search
............................................................................................149
3.1
Optimising the Effective Side-Length Expression for the Resonant
Frequency of a Triangular
Microstrip
Antenna
..........................................149
3.1.1
Formulation
.......................................................................................151
3.1.2
Results and Discussion
.....................................................................155
3.2
Obtaining a Simple Formula for the Radiation Efficiency of a Resonant
Rectangular
Microstrip
Antenna
................................................................157
3.2.1
Radiation Efficiency of Rectangular
Microstrip
Antennas
...............159
3.2.2
Application of Tabu Search to the Problem
......................................160
3.2.3
Simulation Results and Discussion
...................................................164
3.3
Training Recurrent Neural Networks for System Identification
................165
3.3.1
Parallel Tabu Search
.........................................................................165
3.3.2
Crossover Operator
...........................................................................166
3.3.3
Training the Elman Network
.............................................................167
3.3.4
Simulation Results and Discussion
...................................................168
3.4
Designing Digital Finite-Impulse-Response Filters
...................................173
3.4.1
FIR Filter Design Problem
................................................................173
3.4.2
Solution by Tabu Search
...................................................................174
3.4.3
Simulation Results
............................................................................175
3.5
Tuning
PID
Controller Parameters
............................................................177
Contents ix
3.5.1
Application
of Tabu Search to the
Problem....................................178
3.5.2
Simulation Results
..........................................................................179
3.6
Summary
....................................................................................................181
References
.........................................................................................................182
4
Simulated Annealing
............................................................................187
4.1
Optimal Alignment of Laser Chip and Optical Fibre
..................................187
4.1.1
Background
.......................................................................................187
4.1.2
Experimental Setup
...........................................................................188
4.1.3
Initial Results
....................................................................................192
4.1.4
Modification of Generation Mechanism
...........................................193
4.1.5
Modification of Cooling Schedule
....................................................193
4.1.6
Starting Point
....................................................................................194
4.1.7
Final Modifications to the Algorithm
...............................................195
4.1.8
Results
..............................................................................................197
4.2
Inspection Stations Allocation and Sequencing
.........................................197
4.2.1
Background
.......................................................................................198
4.2.2
Transfer Functions Model
.................................................................200
4.2.3
Problem Description
.........................................................................202
4.2.4
Application of Simulated Annealing
.................................................204
4.2.5
Experimentation and Results
............................................................206
4.3
Economic Lot-Size Production
...................................................................209
4.3.1
Economic Lot-Size Production Model
..............................................210
4.3.2
Implementation to Economic Lot-Size Production
...........................213
4.4
Summary
.....................................................................................................217
References
.........................................................................................................217
5
Neural Networks
....................................................................................219
5.1
VLSI Placement using MHSO Networks
....................................................219
5.1.1
Placement System Based on Mapping Self-Organising Network
.....221
5.1.2
Hierarchical Neural Network for Macro Cell Placement
..................225
5.1.3
MHSO2 Experiments
........................................................................228
5.2
Satellite Broadcast Scheduling using a Hopfield Network
.........................230
5.2.1
Problem Definition
...........................................................................231
5.2.2
Neural-Network Approach
................................................................233
5.2.3
Simulation Results
............................................................................235
5.3
Summary
.....................................................................................................238
References
.........................................................................................................238
χ
Contents
Appendix 1
Classical Optimisation
....................................................241
ALI
Basic Definitions
......................................................................................241
A1.2 Classification of Problems
.......................................................................243
A1.3 Classification of Optimisation Techniques
...............................................244
References
.........................................................................................................247
Appendix
2
Fuzzy Logic Control
........................................................249
A2.1 Fuzzy Sets
................................................................................................249
A2.1.1 Fuzzy Set Theory
...........................................................................249
A2.
1.2
Basic Operations on Fuzzy Sets
.....................................................250
A2.2 Fuzzy Relations
........................................................................................253
A2.3 Compositional Rule of Inference
.............................................................254
A2.4 Basic Structure of a Fuzzy Logic Controller
............................................255
A2.5 Studies in Fuzzy Logic Control
................................................................258
References
.........................................................................................................259
Appendix
3
Genetic Algorithm Program
........................................263
Appendix
4
Tabu Search Program
....................................................271
Appendix
5
Simulated Annealing Program
...................................279
Appendix
6
Neural Network Programs
...........................................285
Author Index
..............................................................................................295
Subject Index
..............................................................................................299
|
| any_adam_object | 1 |
| author | Pham, Duc Truong 1952- Karaboga, Dervis 1965- |
| author_GND | (DE-588)121726029 (DE-588)12172610X |
| author_facet | Pham, Duc Truong 1952- Karaboga, Dervis 1965- |
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| author_sort | Pham, Duc Truong 1952- |
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| id | DE-604.BV012035733 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T10:23:07Z |
| institution | BVB |
| isbn | 1852330287 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008144544 |
| oclc_num | 610844572 |
| open_access_boolean | |
| owner | DE-29T DE-706 DE-355 DE-BY-UBR DE-634 DE-83 |
| owner_facet | DE-29T DE-706 DE-355 DE-BY-UBR DE-634 DE-83 |
| physical | X, 302 S. zahlr. graph. Darst. |
| publishDate | 2000 |
| publishDateSearch | 2000 |
| publishDateSort | 2000 |
| publisher | Springer |
| record_format | marc |
| spellingShingle | Pham, Duc Truong 1952- Karaboga, Dervis 1965- Intelligent optimisation techniques genetic algorithms, tabu search, simulated annealing and neural networks Datenverarbeitung Ingenieurwissenschaften Computer-aided engineering Engineering Data processing Genetic algorithms Heuristic programming Neural networks (Computer science) Simulated annealing (Mathematics) Tabusuche (DE-588)4432514-9 gnd Simulated annealing (DE-588)4265091-4 gnd Neuronales Netz (DE-588)4226127-2 gnd Genetischer Algorithmus (DE-588)4265092-6 gnd Optimierung (DE-588)4043664-0 gnd |
| subject_GND | (DE-588)4432514-9 (DE-588)4265091-4 (DE-588)4226127-2 (DE-588)4265092-6 (DE-588)4043664-0 |
| title | Intelligent optimisation techniques genetic algorithms, tabu search, simulated annealing and neural networks |
| title_auth | Intelligent optimisation techniques genetic algorithms, tabu search, simulated annealing and neural networks |
| title_exact_search | Intelligent optimisation techniques genetic algorithms, tabu search, simulated annealing and neural networks |
| title_full | Intelligent optimisation techniques genetic algorithms, tabu search, simulated annealing and neural networks D. T. Pham and D. Karaboga |
| title_fullStr | Intelligent optimisation techniques genetic algorithms, tabu search, simulated annealing and neural networks D. T. Pham and D. Karaboga |
| title_full_unstemmed | Intelligent optimisation techniques genetic algorithms, tabu search, simulated annealing and neural networks D. T. Pham and D. Karaboga |
| title_short | Intelligent optimisation techniques |
| title_sort | intelligent optimisation techniques genetic algorithms tabu search simulated annealing and neural networks |
| title_sub | genetic algorithms, tabu search, simulated annealing and neural networks |
| topic | Datenverarbeitung Ingenieurwissenschaften Computer-aided engineering Engineering Data processing Genetic algorithms Heuristic programming Neural networks (Computer science) Simulated annealing (Mathematics) Tabusuche (DE-588)4432514-9 gnd Simulated annealing (DE-588)4265091-4 gnd Neuronales Netz (DE-588)4226127-2 gnd Genetischer Algorithmus (DE-588)4265092-6 gnd Optimierung (DE-588)4043664-0 gnd |
| topic_facet | Datenverarbeitung Ingenieurwissenschaften Computer-aided engineering Engineering Data processing Genetic algorithms Heuristic programming Neural networks (Computer science) Simulated annealing (Mathematics) Tabusuche Simulated annealing Neuronales Netz Genetischer Algorithmus Optimierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008144544&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT phamductruong intelligentoptimisationtechniquesgeneticalgorithmstabusearchsimulatedannealingandneuralnetworks AT karabogadervis intelligentoptimisationtechniquesgeneticalgorithmstabusearchsimulatedannealingandneuralnetworks |