Intelligent optimisation techniques : genetic algorithms, tabu search, simulated annealing and neural networks
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書誌事項
Intelligent optimisation techniques : genetic algorithms, tabu search, simulated annealing and neural networks
Springer, c2000
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注記
Includes bibliographical references and indexes
内容説明・目次
内容説明
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.
目次
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.
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