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035   $a(DE-He213)978-3-319-11857-4
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100   $a20140903d2014      u|             0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aAdvances in Swarm Intelligence$b[electronic resource] $e5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part I /$fedited by Ying Tan, Yuhui Shi, Carlos Coello Coello
205   $a1st ed. 2014.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014.
215   $a1 online resource (XXIV, 510 p. 162 illus.)
225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v8794
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-319-11856-0 
327   $aNovel swarm-based search methods -- Novel optimization algorithm -- Particle swarm optimization -- Ant colony optimization for travelling salesman problem -- Artificial bee colony algorithms -- Artificial immune system -- Evolutionary algorithms -- Neural networks and fuzzy methods -- Hybrid methods -- Multi-objective optimization -- Multi-agent systems -- Evolutionary clustering algorithms -- Classification methods -- GPU-based methods -- Scheduling and path planning -- Wireless sensor networks -- Power system optimization -- Swarm intelligence in image and video processing -- Applications of swarm intelligence to management problems -- Swarm intelligence for real-world application.
330   $aThis book and its companion volume, LNCS vol. 8794 and 8795 constitute the proceedings of the 5th International Conference on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised full papers presented were carefully reviewed and selected from 198 submissions. The papers are organized in 18 cohesive sections, 3 special sessions and one competitive session covering all major topics of swarm intelligence research and development such as novel swarm-based search methods; novel optimization algorithm; particle swarm optimization; ant colony optimization for travelling salesman problem; artificial bee colony algorithms; artificial immune system; evolutionary algorithms; neural networks and fuzzy methods; hybrid methods; multi-objective optimization; multi-agent systems; evolutionary clustering algorithms; classification methods; GPU-based methods; scheduling and path planning; wireless sensor networks; power system optimization; swarm intelligence in image and video processing; applications of swarm intelligence to management problems; swarm intelligence for real-world application.
410  0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v8794
606   $aAlgorithms
606   $aNumerical analysis
606   $aComputer science?Mathematics
606   $aDiscrete mathematics
606   $aData mining
606   $aArtificial intelligence
606   $aAlgorithms
606   $aNumerical Analysis
606   $aDiscrete Mathematics in Computer Science
606   $aData Mining and Knowledge Discovery
606   $aArtificial Intelligence
615  0$aAlgorithms.
615  0$aNumerical analysis.
615  0$aComputer science?Mathematics.
615  0$aDiscrete mathematics.
615  0$aData mining.
615  0$aArtificial intelligence.
615 14$aAlgorithms.
615 24$aNumerical Analysis.
615 24$aDiscrete Mathematics in Computer Science.
615 24$aData Mining and Knowledge Discovery.
615 24$aArtificial Intelligence.
676   $a004.071
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   $aCoello Coello$b Carlos$4edt$4http://id.loc.gov/vocabulary/relators/edt
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a996202525203316
996   $aAdvances in Swarm Intelligence$92005634
997   $aUNISA