LEADER 03440nam 22005175 450 001 9910299940403321 005 20200630172808.0 010 $a3-319-70851-1 024 7 $a10.1007/978-3-319-70851-5 035 $a(CKB)4100000002892106 035 $a(MiAaPQ)EBC5358048 035 $a(DE-He213)978-3-319-70851-5 035 $a(PPN)225551500 035 $a(EXLCZ)994100000002892106 100 $a20180314d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic /$fby Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (110 pages) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3704 311 $a3-319-70850-3 327 $aIntroduction -- Theory and Background -- Problems Statement -- Methodology -- Simulation Results -- Statistical Analysis and Comparison of Results. 330 $aIn this book, a methodology for parameter adaptation in meta-heuristic op-timization methods is proposed. This methodology is based on using met-rics about the population of the meta-heuristic methods, to decide through a fuzzy inference system the best parameter values that were carefully se-lected to be adjusted. With this modification of parameters we want to find a better model of the behavior of the optimization method, because with the modification of parameters, these will affect directly the way in which the global or local search are performed. Three different optimization methods were used to verify the improve-ment of the proposed methodology. In this case the optimization methods are: PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization) and GSA (Gravitational Search Algorithm), where some parameters are se-lected to be dynamically adjusted, and these parameters have the most im-pact in the behavior of each optimization method. Simulation results show that the proposed methodology helps to each optimization method in obtaining better results than the results obtained by the original method without parameter adjustment. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3704 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 $a006.3 700 $aOlivas$b Frumen$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063586 702 $aValdez$b Fevrier$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCastillo$b Oscar$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aMelin$b Patricia$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299940403321 996 $aDynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic$92533065 997 $aUNINA