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020 _a9783319509204
_9978-3-319-50920-4
024 7 _a10.1007/978-3-319-50920-4
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aNature-Inspired Computing and Optimization
_h[electronic resource] :
_bTheory and Applications /
_cedited by Srikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXXI, 494 p. 191 illus., 43 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aModeling and Optimization in Science and Technologies,
_x2196-7334 ;
_v10
505 0 _aFrom the content: The Nature of Nature: Why Nature Inspired Algorithms Work -- Improved Bat Algorithm in Noise-Free and Noisy Environments -- Multi-objective Ant Colony Optimisation in Wireless Sensor Networks.le.
520 _aThe book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals.
650 0 _aComputational intelligence.
_97716
650 0 _aMathematical optimization.
_94112
650 0 _aArtificial intelligence.
_93407
650 0 _aComputer simulation.
_95106
650 0 _aIndustrial Management.
_95847
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aOptimization.
_956878
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputer Modelling.
_956879
650 2 4 _aIndustrial Management.
_95847
700 1 _aPatnaik, Srikanta.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_956880
700 1 _aYang, Xin-She.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_956881
700 1 _aNakamatsu, Kazumi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_956882
710 2 _aSpringerLink (Online service)
_956883
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319509198
776 0 8 _iPrinted edition:
_z9783319509211
776 0 8 _iPrinted edition:
_z9783319845227
830 0 _aModeling and Optimization in Science and Technologies,
_x2196-7334 ;
_v10
_956884
856 4 0 _uhttps://doi.org/10.1007/978-3-319-50920-4
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79832
_d79832