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100   $a20180615d2018      u|             0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 10$aAdvances in Swarm Intelligence$b[electronic resource] $e9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part II /$fedited by Ying Tan, Yuhui Shi, Qirong Tang
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (XXIV, 579 p. 247 illus.) 
225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v10942
311   $a3-319-93817-7 
320   $aIncludes bibliographical references and index.
327   $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. .
330   $aThe two-volume set of LNCS 10941 and 10942 constitutes the proceedings of the 9th International Conference on Advances in Swarm Intelligence, ICSI 2018, held in Shanghai, China, in June 2018. The total of 113 papers presented in these volumes was carefully reviewed and selected from 197 submissions. The papers were organized in topical sections namely: multi-agent systems; swarm robotics; fuzzy logic approaches; planning and routing problems; recommendation in social media; predication; classification; finding patterns; image enhancement; deep learning; theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithm; bacterial foraging optimization; artificial immune system; hydrologic cycle optimization; other swarm-based optimization algorithms; hybrid optimization algorithms; multi-objective optimization; large-scale global optimization.
410  0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v10942
606   $aAlgorithms
606   $aArtificial intelligence
606   $aComputer networks
606   $aComputer engineering
606   $aAlgorithms
606   $aArtificial Intelligence
606   $aComputer Communication Networks
606   $aComputer Engineering and Networks
615  0$aAlgorithms.
615  0$aArtificial intelligence.
615  0$aComputer networks.
615  0$aComputer engineering.
615 14$aAlgorithms.
615 24$aArtificial Intelligence.
615 24$aComputer Communication Networks.
615 24$aComputer Engineering and Networks.
676   $a006.3
702   $aTan$b Ying$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aShi$b Yuhui$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aTang$b Qirong$4edt$4http://id.loc.gov/vocabulary/relators/edt
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906   $aBOOK
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996   $aAdvances in Swarm Intelligence$92005634
997   $aUNISA