Ant Colony Optimization is a method of generating algorithms inspired by the collective behaviour of ants. First proposed by Marco Dorigo in 1992, the method has grown over the past two decades to become one of the most popular nature-inspired algorithmic techniques, and today this meta-heuristic method is applied to hard optimization problems in a variety of fields, ranging from artificial intelligence to manufacturing. This book covers theoretical principles and practical applications of Ant Colony Optimization, making it an excellent introductory text for more advanced students of computer science, as well as investigators working with optimization problems in transport, planning and image processing. The first section gives detailed accounts of techniques such as parallel Ant Colony Optimization and feature selection, as well as demonstrating practical applications in areas such as traffic control and graphics processing. The second section focuses on methods and applications. Topics discussed include Ant Colony Optimization for image segmentation and techniques for multi-objective optimization.
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