Intelligent Optimisation Techniques

This book covers four optimisation techniques loosely classified as "intelligent": genetic algorithms, tabu search, simulated annealing and neural networks. • Genetic algorithms (GAs) locate optima using processes similar to those in natural selection and genetics. • Tabu search is a heuristic procedure that employs dynamically generated constraints or tabus to guide the search for optimum solutions. • Simulated annealing finds optima in a way analogous to the reaching of minimum energy configurations in metal annealing. • Neural networks are computational models of the brain. Certain types of neural networks can be used for optimisation by exploiting their inherent ability to evolve in the direction of the negative gradient of an energy function and to reach a stable minimum of that function. Aimed at engineers, the book gives a concise introduction to the four techniques and presents a range of applications drawn from electrical, electronic, manufacturing, mechanical and systems engineering. The book contains listings of C programs implementing the main techniques described to assist readers wishing to experiment with them. The book does not assume a previous background in intelligent optl1TIlsation techniques. For readers unfamiliar with those techniques, Chapter 1 outlines the key concepts underpinning them. To provide a common framework for comparing the different techniques, the chapter describes their performances on simple benchmark numerical and combinatorial problems. More complex engineering applications are covered in the remaining four chapters of the book.

The optimisation techniques covered are topical, modern and widely used in various engineering disciplines Details all four techniques in one work Presents a number of applications from a wide range of engineering disciplines

Inhalt
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.