文档介绍：Ant 6/9/04 12:15 PM Page 1
Ant Colony Optimization Marco Dorigo and Thomas Stützle Ant Colony Optimization
plex social behaviors of ants have been much studied by science, puter scientists are now finding that
these behavior patterns can provide models for solving binatorial optimization problems. The attempt to
develop algorithms inspired by one aspect of ant behavior, the ability to find puter scientists would call shortest
paths, has e the field of ant colony optimization (ACO), the most essful and widely recognized algorithmic Ant Colony
technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception
to practical applications, including descriptions of many available ACO algorithms and their uses.
The book first describes the translation of observed ant behavior into working optimization algorithms. The ant Optimization
colony metaheuristic is then introduced and viewed in the general context binatorial 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 bioin-
formatics problems. , an ACO algorithm designed for work routing problem, is described in detail. The
authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends Marco Dorigo and Thomas Stützle
with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony
Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to
learn how to implement ACO algorithms.
Marco Dorigo is research director of the IRIDIA lab at the Université Libre de Bruxelles and the inventor of the ant
colony optimization metaheuristic binatorial optimi