Picture 1 of 1

Stock photo

Ant Colony Optimization - Picture 1 of 1

Ant Colony Optimization by Marco Dorigo and Thomas Stutzle (2004, Hardcover)

Brenham Book Company (465)

93.6% positive feedback

Price:

$109.84

+ $4.25 shipping

Estimated delivery Wed, Jun 4 - Tue, Jun 10

Returns:

30 days returns. Buyer pays for return shipping. If you use an eBay shipping label, it will be deducted from your refund amount.

Condition:

Brand New

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.

About this product

Product Identifiers

PublisherMIT Press

ISBN-100262042193

ISBN-139780262042192

eBay Product ID (ePID)6035648

Product Key Features

Number of Pages319 Pages

LanguageEnglish

Publication NameAnt Colony Optimization

SubjectAnimals / Insects & Spiders, Computer Science, Statistics, Robotics, Optimization

Publication Year2004

TypeTextbook

AuthorMarco Dorigo, Thomas Stutzle

Subject AreaMathematics, Nature, Computers, Technology & Engineering, Psychology

FormatHardcover

Dimensions

Item Height0.8 in

Item Weight22.9 Oz

Item Length9.3 in

Item Width7.2 in

Additional Product Features

Intended AudienceTrade

LCCN2003-066629

Dewey Edition22

Reviews"Marco Dorigo and Thomas Stutzle impressively demonstrate that the importance of ant behavior reaches far beyond the sociobiological domain. Ant Colony Optimizationpresents the most successful algorithmic 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." -Bert Holldobler, Professor of Behavioral Physiology and Sociobiology, Biozentrum, University of Wurzburg, Germany

Grade FromCollege Graduate Student

IllustratedYes

Dewey Decimal519.6

SynopsisAn 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., An overview of the rapidly growing field of Ant Colony Optimization that describes theoretical findings, the major algorithms, and current applications.

LC Classification NumberQA402.5.D64 2004