LEADER 03686nam 22005895 450 001 9910299935703321 005 20251113210310.0 010 $a3-319-89309-2 024 7 $a10.1007/978-3-319-89309-9 035 $a(CKB)4100000003359633 035 $a(DE-He213)978-3-319-89309-9 035 $a(MiAaPQ)EBC6298138 035 $a(MiAaPQ)EBC5578269 035 $a(Au-PeEL)EBL5578269 035 $a(OCoLC)1066198628 035 $a(PPN)226696804 035 $a(EXLCZ)994100000003359633 100 $a20180410d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Metaheuristics Algorithms: Methods and Applications /$fby Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XIV, 218 p. 48 illus., 13 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v775 300 $aIncludes index. 311 08$a3-319-89308-4 327 $aIntroduction -- The metaheuristic algorithm of the social-spider -- Calibration of Fractional Fuzzy Controllers by using the Social-spider method -- The metaheuristic algorithm of the Locust-search -- Identification of fractional chaotic systems by using the Locust Search Algorithm -- The States of Matter Search (SMS) -- Multimodal States of Matter search -- Metaheuristic algorithms based on Fuzzy Logic. 330 $aThis book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v775 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 $a519.6 700 $aCuevas$b Erik$4aut$4http://id.loc.gov/vocabulary/relators/aut$0761169 702 $aZaldívar$b Daniel$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aPérez-Cisneros$b Marco$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299935703321 996 $aAdvances in Metaheuristics Algorithms: Methods and Applications$92504749 997 $aUNINA