ANT COLONY OPTIMIZATION MARCO DORIGO AND THOMAS STTZLE PDF

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.

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A Bradford Book. 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.

This is essential reading not only for those working in artificial intelligence and optimization, but for all of us who find the interface between biology and technology fascinating. Ant Colony Optimization presents the most successful algortihmic techniques to be developed on the basis on ant behavior.

This book will certainly open the gates for new experimental work on decision making, division of labor, and communication; moreover, it will also inspire all those studying patterns of self-organization. Search Search.

Search Advanced Search close Close. Preview Preview. Add to Cart Buying Options. Request Permissions Exam copy. Overview Author s Praise. Summary An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. June Share Share Share email. He is the inventor of the ant colony optimization metaheuristic.

His current research interests include swarm intelligence, swarm robotics, and metaheuristics for discrete optimization. He is the Editor-in-Chief of Swarm Intelligence, and an Associate Editor or member of the Editorial Boards of many journals on computational intelligence and adaptive systems. Iain D.

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