Intelligent optimisation techniques. Genetic algorithms, tabu search, simulated annealing and neural networks.

*(English)*Zbl 0986.90001
London: Springer. x, 302 p. (2000).

Among the “Intelligent Optimisation Techniques” presented in this monograph are genetic algorithms, tabu search, simulated-annealing, and neural networks. Genetic algorithms 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 optimization 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.

The authors give a concise introduction to the four techniques and present a range of applications drawn from electrical, electronic, manufacturing, mechanical and systems engineering. The book, in addition, 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 optimization techniques. The basics of optimization techniques are reviewed in the first chapter of the book. To provide a common framework for comparing the different techniques, the chapter describes their performances on simple benchmark numerical and combinatorial problems. More complex and detailed engineering applications are covered in the remaining four chapters of the book. In addition to the main chapters, the book has six appendices. These appendices provide background material on classical optimization techniques and fuzzy logic theory as well as the C programs.

The authors give a concise introduction to the four techniques and present a range of applications drawn from electrical, electronic, manufacturing, mechanical and systems engineering. The book, in addition, 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 optimization techniques. The basics of optimization techniques are reviewed in the first chapter of the book. To provide a common framework for comparing the different techniques, the chapter describes their performances on simple benchmark numerical and combinatorial problems. More complex and detailed engineering applications are covered in the remaining four chapters of the book. In addition to the main chapters, the book has six appendices. These appendices provide background material on classical optimization techniques and fuzzy logic theory as well as the C programs.

Reviewer: Herbert S.Buscher (Mannheim)

##### MSC:

90-01 | Introductory exposition (textbooks, tutorial papers, etc.) pertaining to operations research and mathematical programming |

90Cxx | Mathematical programming |

90C59 | Approximation methods and heuristics in mathematical programming |

90C57 | Polyhedral combinatorics, branch-and-bound, branch-and-cut |

90C70 | Fuzzy and other nonstochastic uncertainty mathematical programming |

92B20 | Neural networks for/in biological studies, artificial life and related topics |

68T05 | Learning and adaptive systems in artificial intelligence |