An Introduction to Metaheuristics for Optimization

This book proposes an introduction to metaheuristics, combining a theoretical understanding with the practical skill to use and develop these methods. Optimization is central to most domains of science, whether academic or industrial. The solution to many real life problems rely on our ability to find the maximum or minimum of some quantity of interest. However, many of these problems are referred to as “hard optimization” problems, meaning that they quickly become numerically intractable and cannot be solved by traditional optimization techniques. Metaheuristics are methods, inspired by physical processes, Darwinian evolution, animal behaviors, and other phenomena observed in Nature, which usually find optimal values of satisfactory quality within acceptable computing resources. As such, they are an essential tool for the optimization community. This textbook is suitable for advanced undergraduates in computer science and engineering, as well as for students and researchers from other disciplines looking for a concise and clear introduction to metaheuristic methods for optimization.

Table of Contents:
Problems, Algorithms, and Computational Complexity.- Search Space.- Statistical Features and Metrics of Search Spaces.- Tabu Search.- Simulated Annealing.- The Ant Colony Method.- Particle Swarm Optimization.- Fireflies, Cuckoos, and Lévy Flights.- Evolutionary Algorithms: Foundations.- Evolutionary Algorithms: structured populations.- Real Function Optimization: Evolution Strategies and Differential Evolution.- Genetic Programming.- Performance and Limitations of Metaheuristics.- Phase Transitions in Combinatorial Optimization Problems.- Adiabatic Quantum Computing and Quantum Annealing.

About the Author :

Bastien Chopard earned his Ph.D. in theoretical physics before spending 2 years as a postdoc at the Laboratory for Computer Science at MIT and 1 year in the supercomputing center in Juelich, Germany. Then he joined the Computer Science Department at the University of Geneva, where he later became a full professor. His research interests comprise the modeling and simulation of complex systems with their applications to multidisciplinary domains of science, such as physics, environment, life sciences, and social systems. During his career, he taught programming courses, high performance computing, algorithms, modeling of natural phenomena, and metaheuristics, always combining a rigorous theoretical approach with a problem solving ambition.

He published over 300 scientific articles, including authored and edited books and was part of many international projects and conference organizations. He is now emeritus professor at the University of Geneva.

Marco Tomassini is an emeritus professor of Computer Science at the Information Systems Department of the University of Lausanne, Switzerland. He obtained a doctoral degree in Chemical Physics working on computer simulations of molecular crystals and macromolecules.
His present research interests are centered on complex networks and complex systems. In particular, the structure and optimization of hard combinatorial problems, the robustness of complex networks, and evolutionary games in networks. He has lectured on scientific computing, evolutionary computation, complex systems, game theory, and optimization metaheuristics in general. He has been the program chairman of several international events and has published many scientific papers and several authored and edited books in these fields. He has received the EvoStar 2010 Award in recognition for outstanding contribution to evolutionary computation.