LEADER 03647nam 22006615 450 001 9910299695903321 005 20200701145934.0 010 $a3-662-46309-1 024 7 $a10.1007/978-3-662-46309-3 035 $a(CKB)3710000000379729 035 $a(SSID)ssj0001465620 035 $a(PQKBManifestationID)11821051 035 $a(PQKBTitleCode)TC0001465620 035 $a(PQKBWorkID)11479747 035 $a(PQKB)10126769 035 $a(DE-He213)978-3-662-46309-3 035 $a(MiAaPQ)EBC6294764 035 $a(MiAaPQ)EBC5590593 035 $a(Au-PeEL)EBL5590593 035 $a(OCoLC)905219397 035 $a(PPN)184895332 035 $a(EXLCZ)993710000000379729 100 $a20150310d2015 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMulti-objective Swarm Intelligence$b[electronic resource] $eTheoretical Advances and Applications /$fedited by Satchidananda Dehuri, Alok Kumar Jagadev, Mrutyunjaya Panda 205 $a1st ed. 2015. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2015. 215 $a1 online resource (XIV, 201 p. 60 illus., 11 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v592 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-662-46308-3 320 $aIncludes bibliographical references. 327 $aIntroduction -- Behavior of Bacterial Colony -- E.coli Bacterial Colonies -- Optimization based on E.coli Bacterial Colony -- Classification of BFO Algorithm -- Multi-objective optimization based on BF -- An overview of BFO Applications -- Conclusion. 330 $aThe aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.       . 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v592 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a006.3824 702 $aDehuri$b Satchidananda$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJagadev$b Alok Kumar$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPanda$b Mrutyunjaya$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299695903321 996 $aMulti-objective Swarm Intelligence$91465802 997 $aUNINA