
Intelligent Optimisation Techniques
Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
Springer (Publisher)
Published on 1. December 1999
Book
Hardback
X, 302 pages
978-1-85233-028-6 (ISBN)
Description
This work gives a concise introduction to four important optimization techniques, presenting a range of applications drawn from electrical, manufacturing, mechanical, and systems engineering-such as the design of microstrip antennas, digital FIR filters, and fuzzy logic controllers. The book also contains the C programs used to implement the main techniques for those wishing to experiment with them.
More details
Edition
1st Edition.
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Research
Illustrations
115 s/w Abbildungen
biography
Dimensions
Height: 240 mm
Weight
620 gr
ISBN-13
978-1-85233-028-6 (9781852330286)
DOI
10.1007/978-1-4471-0721-7
Schweitzer Classification
Other editions
Additional editions

Duc Pham | D. Karaboga
Intelligent Optimisation Techniques
Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
E-Book
12/2012
Springer
€96.29
Available for download

Duc Pham | D. Karaboga
Intelligent Optimisation Techniques
Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
Book
09/2011
Springer
€106.99
Shipment within 15-20 days
Content
1 Introduction.- 1.1 Genetic Algorithms.- 1.1.1 Background.- 1.1.2 Representation.- 1.1.3 Creation of Initial Population.- 1.1.4 Genetic Operators.- 1.1.5 Control Parameters.- 1.1.6 Fitness Evaluation Function.- 1.2 Tabu Search.- 1.2.1 Background.- 1.2.2 Strategies.- 1.3 Simulated Annealing.- 1.3.1 Background.- 1.3.2 Basic Elements.- 1.4 Neural Networks.- 1.4.1 Basic Unit.- 1.4.2 Structural Categorisation.- 1.4.3 Learning Algorithm Categorisation.- 1.4.4 Optimisation Algorithms.- 1.4.5 Example Neural Networks.- 1.5 Performance of Different Optimisation Techniques on Benchmark Test Functions.- 1.5.1 Genetic Algorithm Used.- 1.5.2 Tabu Search Algorithm Used.- 1.5.3 Simulated Annealing Algorithm Used.- 1.5.4 Neural Network Used.- 1.5.5 Results.- 1.6 Performance of Different Optimisation Techniques on Travelling Salesman Problem.- 1.6.1 Genetic Algorithm Used.- 1.6.2 Tabu Search Algorithm Used.- 1.6.3 Simulated Annealing Algorithm Used.- 1.6.4 Neural Network Used.- 1.6.5 Results.- 1.7 Summary.- References.- 2 Genetic Algorithms.- 2.1 New Models.- 2.1.1 Hybrid Genetic Algorithm.- 2.1.2 Cross-Breeding in Genetic Optimisation.- 2.1.3 Genetic Algorithm with the Ability to Increase the Number of Alternative Solutions.- 2.1.4 Genetic Algorithms with Variable Mutation Rates.- 2.2 Engineering Applications.- 2.2.1 Design of Static Fuzzy Logic Controllers.- 2.2.2 Training Recurrent Neural Networks.- 2.2.3 Adaptive Fuzzy Logic Controller Design.- 2.2.4 Preliminary Gearbox Design.- 2.2.5 Ergonomic Workplace Layout Design.- 2.3 Summary.- References.- 3 Tabu Search.- 3.1 Optimising the Effective Side-Length Expression for the Resonant Frequency of a Triangular Microstrip Antenna.- 3.1.1 Formulation.- 3.1.2 Results and Discussion.- 3.2 Obtaining a Simple Formula for the Radiation Efficiency of a Resonant Rectangular Microstrip Antenna.- 3.2.1 Radiation Efficiency of Rectangular Microstrip Antennas.- 3.2.2 Application of Tabu Search to the Problem.- 3.2.3 Simulation Results and Discussion.- 3.3 Training Recurrent Neural Networks for System Identification.- 3.3.1 Parallel Tabu Search.- 3.3.2 Crossover Operator.- 3.3.3 Training the Elman Network.- 3.3.4 Simulation Results and Discussion.- 3.4 Designing Digital Finite-Impulse-Response Filters.- 3.4.1 FIR Filter Design Problem.- 3.4.2 Solution by Tabu Search.- 3.4.3 Simulation Results.- 3.5 Tuning PID Controller Parameters.- 3.5.1 Application of Tabu Search to the Problem.- 3.5.2 Simulation Results.- 3.6 Summary.- References.- 4 Simulated Annealing.- 4.1 Optimal Alignment of Laser Chip and Optical Fibre.- 4.1.1 Background.- 4.1.2 Experimental Setup.- 4.1.3 Initial Results.- 4.1.4 Modification of Generation Mechanism.- 4.1.5 Modification of Cooling Schedule.- 4.1.6 Starting Point.- 4.1.7 Final Modifications to the Algorithm.- 4.1.8 Results.- 4.2 Inspection Stations Allocation and Sequencing.- 4.2.1 Background.- 4.2.2 Transfer Functions Model.- 4.2.3 Problem Description.- 4.2.4 Application of Simulated Annealing.- 4.2.5 Experimentation and Results.- 4.3 Economic Lot-Size Production.- 4.3.1 Economic Lot-Size Production Model.- 4.3.2 Implementation to Economic Lot-Size Production.- 4.4 Summary.- References.- 5 Neural Networks.- 5.1 VLSI Placement using MHSO Networks.- 5.1.1 Placement System Based on Mapping Self-Organising Network.- 5.1.2 Hierarchical Neural Network for Macro Cell Placement.- 5.1.3 MHSO2 Experiments.- 5.2 Satellite Broadcast Scheduling using a Hopfield Network.- 5.2.1 Problem Definition.- 5.2.2 Neural-Network Approach.- 5.2.3 Simulation Results.- 5.3 Summary.- References.- Appendix 1 Classical Optimisation.- A1.1 Basic Definitions.- A1.2 Classification of Problems.- A1.3 Classification of Optimisation Techniques.- References.- Appendix 2 Fuzzy Logic Control.- A2.1 Fuzzy Sets.- A2.1.1 Fuzzy Set Theory.- A2.1.2 Basic Operations on Fuzzy Sets.- A2.2 Fuzzy Relations.- A2.3 Compositional Rule of Inference.- A2.4 Basic Structure of a Fuzzy Logic Controller.- A2.5 Studies in Fuzzy Logic Control.- References.- Appendix 3 Genetic Algorithm Program.- Appendix 4 Tabu Search Program.- Appendix 5 Simulated Annealing Program.- Appendix 6 Neural Network Programs.- Author Index.