000			03237nam a22003497a 4500
003			OSt
005			20250626112859.0
008			250626b |||||||| |||| 00| 0 eng d
020			_a9781558605954 _q(hardback)
040			_beng _cDLC _dDLC _erda _aSai University Library
082			_223 _a006.3 _bKEN
100	1		_aKennedy, James F. _eauthor
245	1	0	_aSwarm intelligence / _cJames Kennedy, Russell C. Eberhart, with Yuhui Shi
260			_aSan Francisco : _bMorgan Kaufmann Publishers, _c2001
264		1	_aSan Francisco : _bMorgan Kaufmann Publishers, _c2001
264		4	_c©2001
300			_axxvii, 512 pages : _billustrations ;
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
490			_aMorgan Kaufmann series in evolutionary computation
500			_aGifted by Ajith Abraham
504			_aIncludes bibliographical references (pages 475-495) and index.
520			_aTraditional methods for creating intelligent computational systems have privileged private "internal" cognitive and computational processes. In contrast, Swarm Intelligence argues that human intelligence derives from the interactions of individuals in a social world and further, that this model of intelligence can be effectively applied to artificially intelligent systems. The authors first present the foundations of this new approach through an extensive review of the critical literature in social psychology, cognitive science, and evolutionary computation. They then show in detail how these theories and models apply to a new computational intelligence methodologyparticle swarmswhich focuses on adaptation as the key behavior of intelligent systems. Drilling down still further, the authors describe the practical benefits of applying particle swarm optimization to a range of engineering problems. Developed by the authors, this algorithm is an extension of cellular automata and provides a powerful optimization, learning, and problem solving method. This important book presents valuable new insights by exploring the boundaries shared by cognitive science, social psychology, artificial life, artificial intelligence, and evolutionary computation and by applying these insights to the solving of difficult engineering problems. Researchers and graduate students in any of these disciplines will find the material intriguing, provocative, and revealing as will the curious and savvy computing professional. * Places particle swarms within the larger context of intelligent adaptive behavior and evolutionary computation. * Describes recent results of experiments with the particle swarm optimization (PSO) algorithm * Includes a basic overview of statistics to ensure readers can properly analyze the results of their own experiments using the algorithm. * Support software which can be downloaded from the publishers website, includes a Java PSO applet, C and Visual Basic source code.
650		0	_aSwarm intelligence
650		0	_aSystems engineering
650		0	_aDistributed artificial intelligence
700	1		_aEberhart, Russell C. _eauthor
700	1		_aShi, Yuhui. _eauthor
830		0	_aMorgan Kaufmann series in evolutionary computation
942			_2ddc _cBK
999			_c6823 _d6823