An Introduction to Metaheuristics for Optimization

The authors stress the relative simplicity, efficiency, flexibility of use, and suitability of various approaches used to solve difficult optimization problems. The authors are experienced, interdisciplinary lecturers and researchers and in their explanations they demonstrate many shared foundational concepts among the key methodologies.

This textbook is a suitable introduction for undergraduate and graduate students, researchers, and professionals in computer science, engineering, and logistics.

Accessible introduction, suitable for students and for self-study Assumes basic computing skills only as a prerequisite Authors are interdisciplinary researchers, and they bring this into their approach

Autorentext

Bastien Chopard is a professor in the Département d'informatique of the Université de Genève, where he directs the Scientific and Parallel Computing Group. His main research activity concerns the study of complex systems, in particular the development of new numerical methods to model and simulate phenomena in natural sciences, economics, social systems, and biomedical applications, the key tools used being cellular automata, the lattice Boltzmann method, and multiagent techniques.

Marco Tomassini is an honorary professor at the Faculty of Business and Economics of the University of Lausanne (HEC). After obtaining a PhD degree in Theoretical Chemistry working on computer simulations of condensed matter systems, he switched to Computer Science and complex systems investigations. His main research activities have since focused on parallel computing, cellular automata, evolutionary algorithms, the structure of difficult problem landscapes, complex networks, and evolutionary games.

Klappentext

The authors stress the relative simplicity, efficiency, flexibility of use, and suitability of various approaches used to solve difficult optimization problems. The authors are experienced, interdisciplinary lecturers and researchers and in their explanations they demonstrate many shared foundational concepts among the key methodologies.

This textbook is a suitable introduction for undergraduate and graduate students, researchers, and professionals in computer science, engineering, and logistics.

Inhalt

Problems, Algorithms, Computational Complexity.- Search Space.- Tabu Search.- Simulated Annealing.- Ant Colony Optimization (ACO).- Non-PSO Optimization.- Firefly Algorithm, Cuckoo Algorithm, Lévy Flights.- Evolutionary Algorithms: Foundations.- Evolutionary Algorithms: Advanced.- Phase Transition in Optimization Problems.- Performance and Limitations of Metaheuristics.- Statistical Analysis of Research Spaces.