LEADER 04448nam 22007215 450 001 9910254330003321 005 20200630175459.0 024 7 $a10.1007/978-3-319-50920-4 035 $a(CKB)3710000001095333 035 $a(DE-He213)978-3-319-50920-4 035 $a(MiAaPQ)EBC4820330 035 $a(PPN)199768633 035 $a(EXLCZ)993710000001095333 100 $a20170308d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNature-Inspired Computing and Optimization $eTheory and Applications /$fedited by Srikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XXI, 494 p. 191 illus., 43 illus. in color.) 225 1 $aModeling and Optimization in Science and Technologies,$x2196-7326 ;$v10 311 $a3-319-50919-5 311 $a3-319-50920-9 320 $aIncludes bibliographical references at the end of each chapters. 327 $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. 330 $aThe book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals. 410 0$aModeling and Optimization in Science and Technologies,$x2196-7326 ;$v10 606 $aComputational intelligence 606 $aMathematical optimization 606 $aArtificial intelligence 606 $aComputer simulation 606 $aEngineering economy 606 $aEngineering economy 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aEngineering Economics, Organization, Logistics, Marketing$3https://scigraph.springernature.com/ontologies/product-market-codes/T22016 615 0$aComputational intelligence. 615 0$aMathematical optimization. 615 0$aArtificial intelligence. 615 0$aComputer simulation. 615 0$aEngineering economy. 615 0$aEngineering economy. 615 14$aComputational Intelligence. 615 24$aOptimization. 615 24$aArtificial Intelligence. 615 24$aSimulation and Modeling. 615 24$aEngineering Economics, Organization, Logistics, Marketing. 676 $a006.3 702 $aPatnaik$b Srikanta$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYang$b Xin-She$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNakamatsu$b Kazumi$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254330003321 996 $aNature-Inspired Computing and Optimization$91948574 997 $aUNINA