000			03588nam a2200529 i 4500
001			6267250
003			IEEE
005			20190220121645.0
006			m o d
007			cr |n|||||||||
008			151223s2004 maua ob 001 eng d
020			_a9780262256032 _qebook
020			_z141756041X _qelectronic
020			_z0262256037 _qelectronic
020			_z9781417560417 _qelectronic
020			_z9780262042192 _qprint
035			_a(CaBNVSL)mat06267250
035			_a(IDAMS)0b000064818b4202
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA402.5 _b.D64 2004eb
082	0	4	_a519.6 _222
100	1		_aDorigo, Marco, _eauthor.
245	1	0	_aAnt colony optimization / _cMarco Dorigo, Thomas Stu�I�tzle.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc2004.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2004]
300			_a1 PDF (xi, 305 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
500			_a"A Bradford book."
504			_aIncludes bibliographical references (p. [277]-300) and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aThe 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.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
550			_aMade available online by NetLibrary.
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aMathematical optimization.
650		0	_aAnts _xBehavior _xMathematical models.
655		0	_aElectronic books.
700	1		_aStu�I�tzle, Thomas.
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aMIT Press, _epublisher.
710	2		_aNetLibrary, Inc.
776	0	8	_iPrint version _z9780262042192
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267250
999			_c39166 _d39166