| 000 | 03116nam a22004695i 4500 | ||
|---|---|---|---|
| 001 | 978-3-642-17390-5 | ||
| 003 | DE-He213 | ||
| 005 | 20140220083750.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 110204s2011 gw | s |||| 0|eng d | ||
| 020 |
_a9783642173905 _9978-3-642-17390-5 |
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| 024 | 7 |
_a10.1007/978-3-642-17390-5 _2doi |
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| 050 | 4 | _aQ342 | |
| 072 | 7 |
_aUYQ _2bicssc |
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| 072 | 7 |
_aCOM004000 _2bisacsh |
|
| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aPanigrahi, Bijaya Ketan. _eeditor. |
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| 245 | 1 | 0 |
_aHandbook of Swarm Intelligence _h[electronic resource] : _bConcepts, Principles and Applications / _cedited by Bijaya Ketan Panigrahi, Yuhui Shi, Meng-Hiot Lim. |
| 264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011. |
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| 300 |
_aXII, 544 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aAdaptation, Learning, and Optimization, _x1867-4534 ; _v8 |
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| 505 | 0 | _aPart A: Particle Swarm Optimization -- Part B: Bee Colony Optimization -- Part C: Ant Colony Optimization.-Part D: Other Swarm Techniques. | |
| 520 | _aFrom nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more. It is only in recent years that researchers have taken notice of such natural swarming systems as culmination of some form of innate collective intelligence, albeit swarm intelligence (SI) - a metaphor that inspires a myriad of computational problem-solving techniques. In computational intelligence, swarm-like algorithms have been successfully applied to solve many real-world problems in engineering and sciences. This handbook volume serves as a useful foundational as well as consolidatory state-of-art collection of articles in the field from various researchers around the globe. It has a rich collection of contributions pertaining to the theoretical and empirical study of single and multi-objective variants of swarm intelligence based algorithms like particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization algorithm (BFOA), honey bee social foraging algorithms, and harmony search (HS). With chapters describing various applications of SI techniques in real-world engineering problems, this handbook can be a valuable resource for researchers and practitioners, giving an in-depth flavor of what SI is capable of achieving. | ||
| 650 | 0 | _aEngineering. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 1 | 4 | _aEngineering. |
| 650 | 2 | 4 | _aComputational Intelligence. |
| 650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
| 700 | 1 |
_aShi, Yuhui. _eeditor. |
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| 700 | 1 |
_aLim, Meng-Hiot. _eeditor. |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783642173899 |
| 830 | 0 |
_aAdaptation, Learning, and Optimization, _x1867-4534 ; _v8 |
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| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-17390-5 |
| 912 | _aZDB-2-ENG | ||
| 999 |
_c107246 _d107246 |
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