Preface

Global optimization is computationally extremely challenging and, for large instances, exact methods reach their limitations quickly. One of the most well-known probabilistic meta-heuristics is Simulated Annealing (SA). Proposed initially to solve discrete optimization problems, it was later extended to continuous domain. The significant advantage of SA over other solution methods has made it a practical solution method for solving complex optimization problems.

In this book, some advances in SA are presented. More specifically: criteria for the number of evaluated solutions required to reach thermal equilibrium; crystallization heuristics that add a feedback mechanism to the next candidate selection; and estimation of the equilibrium quantities by the average quantity through the nonequilibrium behavior. Subsequent chapters of this book will focus on the applications of SA in signal processing, image processing, electric power systems, operational planning, vehicle routing and farm animal mating. The final chapters combine SA with other techniques to obtain the global optimum: artificial neural networks, genetic algorithms and fuzzy logic.

This book provides the reader with the knowledge of SA and several SA applications. We encourage readers to explore SA in their work, mainly because it is simple and because it can yield very good results.

> **Marcos de Sales Guerra Tsuzuki** Department of Mechatronics and Mechanical Systems Engineering, University of São Paulo, Brazil

**Section 1** 

**Advances in SA**

**Section 1** 
