Intelligent Optimisation Techniques

1 Introduction.- 1.1 Genetic Algorithms.- 1.2 Tabu Search.- 1.3 Simulated Annealing.- 1.4 Neural Networks.- 1.5 Performance of Different Optimisation Techniques on Benchmark Test Functions.- 1.6 Performance of Different Optimisation Techniques on Travelling Salesman Problem.- 1.7 Summary.- 2 Genetic Algorithms.- 2.1 New Models.- 2.2 Engineering Applications.- 2.3 Summary.- 3 Tabu Search.- 3.1 Optimising the Effective Side-Length Expression for the Resonant Frequency of a Triangular Microstrip Antenna.- 3.2 Obtaining a Simple Formula for the Radiation Efficiency of a Resonant Rectangular Microstrip Antenna.- 3.3 Training Recurrent Neural Networks for System Identification.- 3.4 Designing Digital Finite-Impulse-Response Filters.- 3.5 Tuning PID Controller Parameters.- 4 Simulated Annealing.- 4.1 Optimal Alignment of Laser Chip and Optical Fibre.- 4.2 Inspection Stations Allocation and Sequencing.- 4.3 Economic Lot-Size Production.- 4.4 Summary.- 5 Neural Networks.- 5.1 VLSI Placement using MHSO Networks.- 5.2 Satellite Broadcast Scheduling using a Hopfield Network.- 5.3 Summary.- 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.