An overview of the rapidly growing field of ant colony optimization
that describes theoretical findings, the major algorithms, and current
applications.
The complex social behaviors of ants have been much studied by science,
and computer scientists are now finding that these behavior patterns can
provide models for solving difficult combinatorial optimization
problems. The attempt to develop algorithms inspired by one aspect of
ant behavior, the ability to find what computer scientists would call
shortest paths, has become the field of ant colony optimization (ACO),
the most successful and widely recognized algorithmic 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 colony metaheuristic 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 the network routing
problem, is described in detail. The authors conclude by summarizing the
progress in the field and outlining future research directions. Each
chapter ends 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.
