LEADER 03256nam 22005655 450 001 9910863151703321 005 20251113203617.0 010 $a9783030581008 010 $a3030581004 024 7 $a10.1007/978-3-030-58100-8 035 $a(CKB)4100000011491449 035 $a(MiAaPQ)EBC6367934 035 $a(DE-He213)978-3-030-58100-8 035 $a(PPN)25808832X 035 $a(EXLCZ)994100000011491449 100 $a20201005d2021 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMetaheuristic Computation: A Performance Perspective /$fby Erik Cuevas, Primitivo Diaz, Octavio Camarena 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (XIV, 269 p. 93 illus., 31 illus. in color.) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v195 311 08$a9783030580995 311 08$a3030580997 327 $aIntroductory concepts of metaheuristic computation -- Introductory concepts of metaheuristic computation -- A metaheuristic methodology based on fuzzy logic principles. 330 $aThis book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Metaheuristic search methods are so numerous and varied in terms of design and potential applications; however, for such an abundant family of optimization techniques, there seems to be a question which needs to be answered: Which part of the design in a metaheuristic algorithm contributes more to its better performance? Several works that compare the performance among metaheuristic approaches have been reported in the literature. Nevertheless, they suffer from one of the following limitations: (A)Their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. (B) Their conclusions consider only the comparison of their final results which cannot evaluate the nature of a good or bad balance between exploration and exploitation. The objective of this book is to compare the performance of various metaheuristic techniques when they are faced with complex optimization problems extracted from different engineering domains. The material has been compiled from a teaching perspective. 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v195 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a006.3 700 $aCuevas$b Erik$0761169 702 $aDiaz$b Primitivo 702 $aCamarena$b Octavio 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910863151703321 996 $aMetaheuristic computation$92853734 997 $aUNINA