Intelligent Optimisation Techniques

1. Front Matter

   Pages i-x

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2. Introduction

   • D. T. Pham, D. Karaboga

   Pages 1-50

3. Genetic Algorithms

   • D. T. Pham, D. Karaboga

   Pages 51-147

4. Tabu Search

   • D. T. Pham, D. Karaboga

   Pages 149-186

5. Simulated Annealing

   • D. T. Pham, D. Karaboga

   Pages 187-218

6. Neural Networks

   • D. T. Pham, D. Karaboga

   Pages 219-240

7. Back Matter

   Pages 241-302

   PDF

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.

• Comp Sci in Engineering
• algorithms
• manufacturing
• neural networks
• operations research
• systems engineering
• training

• Systems Division, School of Engineering, University of Wales, Cardiff, Cardiff, UK

  D. T. Pham

• Department of Electronic Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey

  D. Karaboga

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