Let’s Know What’s There In The Book
The Book
This book introduces the rapidly growing field of ant colony optimization. It gives a broad overview of many aspects of ACO, ranging from a detailed description of the ideas underlying ACO, to the definition of how ACO can generally be applied to a wide range of combinatorial optimization problems, and describes many of the available ACO algorithms and their main applications.
The book first describes the translation of observed ant behaviour into working optimization algorithms. The ant colony metaheuristics is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for network routing problem, is described in detail. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises.
The book is intended primarily for
(1) academic and industry researchers in operations research, artificial intelligence, and computational intelligences;
(2) practitioners wishing to learn how to implement ACO algorithms to solve combinatorial optimization problems; and
(3) postgraduate students in computer science, management studies, operations research, and artificial intelligence.
The Authors
MARCO DORIGO is research director of IRIDA lab at the Universite Libre de Bruxelles and the inventor of the ant colony optimization metaheuristic for combinatorial optimization problems. He has received the Marie Curie Excellence Award for his research work on ant colony optimization and ant algorithms. He is the coauthor of Robot Shaping (MIT Press, 1998) and Swarm Intelligence.
THOMAS STÜTZLE is Assistant Professor in the Computer Science Department at Darmstadt University of Technology.
The Contents
1. From Real to Artificial Ants
2. The Ant Colony Optimization Metaheuristic
3. Ant Colony Optimization Algorithms for the Travelling Salesman Problem
4. Ant Colony Optimization Theory
5. Ant Colony Optimization for NP-Hard Problems
6. AntNet. An ACO Algorithm for Data Network Routing
7. Conclusions and Prospects for the Future
Appendix: Sources of Information About the ACO Field
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