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008 180410s2018 sz | s |||| 0|eng d
020 _a9783319893099
_9978-3-319-89309-9
024 7 _a10.1007/978-3-319-89309-9
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aCuevas, Erik.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_942471
245 1 0 _aAdvances in Metaheuristics Algorithms: Methods and Applications
_h[electronic resource] /
_cby Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIV, 218 p. 48 illus., 13 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v775
505 0 _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.
520 _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.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aZaldívar, Daniel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_942472
700 1 _aPérez-Cisneros, Marco.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_942473
710 2 _aSpringerLink (Online service)
_942474
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319893082
776 0 8 _iPrinted edition:
_z9783319893105
776 0 8 _iPrinted edition:
_z9783030077365
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v775
_942475
856 4 0 _uhttps://doi.org/10.1007/978-3-319-89309-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c77134
_d77134