000 03486nam a22005415i 4500
001 978-3-319-70851-5
003 DE-He213
005 20220801221155.0
007 cr nn 008mamaa
008 180314s2018 sz | s |||| 0|eng d
020 _a9783319708515
_9978-3-319-70851-5
024 7 _a10.1007/978-3-319-70851-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aOlivas, Frumen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954514
245 1 0 _aDynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic
_h[electronic resource] /
_cby Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aVII, 105 p. 25 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
505 0 _aIntroduction -- Theory and Background -- Problems Statement -- Methodology -- Simulation Results -- Statistical Analysis and Comparison of Results.
520 _aIn this book, a methodology for parameter adaptation in meta-heuristic op-timization methods is proposed. This methodology is based on using met-rics about the population of the meta-heuristic methods, to decide through a fuzzy inference system the best parameter values that were carefully se-lected to be adjusted. With this modification of parameters we want to find a better model of the behavior of the optimization method, because with the modification of parameters, these will affect directly the way in which the global or local search are performed. Three different optimization methods were used to verify the improve-ment of the proposed methodology. In this case the optimization methods are: PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization) and GSA (Gravitational Search Algorithm), where some parameters are se-lected to be dynamically adjusted, and these parameters have the most im-pact in the behavior of each optimization method. Simulation results show that the proposed methodology helps to each optimization method in obtaining better results than the results obtained by the original method without parameter adjustment.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aValdez, Fevrier.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954515
700 1 _aCastillo, Oscar.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954516
700 1 _aMelin, Patricia.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954517
710 2 _aSpringerLink (Online service)
_954518
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319708508
776 0 8 _iPrinted edition:
_z9783319708522
830 0 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
_954519
856 4 0 _uhttps://doi.org/10.1007/978-3-319-70851-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79373
_d79373