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020 _a9783319938189
_9978-3-319-93818-9
024 7 _a10.1007/978-3-319-93818-9
_2doi
050 4 _aQA76.9.A43
072 7 _aUMB
_2bicssc
072 7 _aCOM051300
_2bisacsh
072 7 _aUMB
_2thema
082 0 4 _a518.1
_223
245 1 0 _aAdvances in Swarm Intelligence
_h[electronic resource] :
_b9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part II /
_cedited by Ying Tan, Yuhui Shi, Qirong Tang.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXXIV, 579 p. 247 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTheoretical Computer Science and General Issues,
_x2512-2029 ;
_v10942
505 0 _aMulti-agent systems -- swarm robotics -- fuzzy logic approaches -- planning and routing problems -- recommendation in social media -- predication -- classification -- finding patterns -- image enhancement -- deep learning -- theories and models of swarm intelligence -- ant colony optimization -- particle swarm optimization -- artificial bee colony algorithms -- genetic algorithms -- differential evolution -- fireworks algorithm -- bacterial foraging optimization -- artificial immune system -- hydrologic cycle optimization -- other swarm-based optimization algorithms -- hybrid optimization algorithms -- multi-objective optimization -- large-scale global optimization. .
520 _aThe two-volume set of LNCS 10941 and 10942 constitutes the proceedings of the 9th International Conference on Advances in Swarm Intelligence, ICSI 2018, held in Shanghai, China, in June 2018. The total of 113 papers presented in these volumes was carefully reviewed and selected from 197 submissions. The papers were organized in topical sections namely: multi-agent systems; swarm robotics; fuzzy logic approaches; planning and routing problems; recommendation in social media; predication; classification; finding patterns; image enhancement; deep learning; theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithm; bacterial foraging optimization; artificial immune system; hydrologic cycle optimization; other swarm-based optimization algorithms; hybrid optimization algorithms; multi-objective optimization; large-scale global optimization.
650 0 _aAlgorithms.
650 0 _aArtificial intelligence.
650 0 _aComputer networks .
650 0 _aComputer engineering.
650 1 4 _aAlgorithms.
650 2 4 _aArtificial Intelligence.
650 2 4 _aComputer Communication Networks.
650 2 4 _aComputer Engineering and Networks.
700 1 _aTan, Ying.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aShi, Yuhui.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aTang, Qirong.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319938172
776 0 8 _iPrinted edition:
_z9783319938196
830 0 _aTheoretical Computer Science and General Issues,
_x2512-2029 ;
_v10942
856 4 0 _uhttps://doi.org/10.1007/978-3-319-93818-9
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912 _aZDB-2-SXCS
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