LEADER 00936nam0-22003371i-450- 001 990003650490403321 005 20001010 035 $a000365049 035 $aFED01000365049 035 $a(Aleph)000365049FED01 035 $a000365049 100 $a20000920d1817----km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aHistoire de Napoleon Buonaparte$e1769-1815$fpar una Societe de Gens de Lettres 210 $aParis$cL.G. Michaud$d1817 225 1 $a 300 $aTomi I-IV 610 0 $a1800 monografie 710 12$aSociété de Gens de lettres$0494181 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003650490403321 952 $aSE 001.03.01-1$fDECSE 952 $aSE 001.03.01-2$fDECSE 952 $aSE 001.03.01-3$fDECSE 952 $aSE 001.03.01-4$fDECSE 959 $aDECSE 996 $aHistoire de Napoleon Buonaparte$9504127 997 $aUNINA DB $aING01 LEADER 03402nam 22005655 450 001 9911003590403321 005 20250518130240.0 010 $a3-031-89284-4 024 7 $a10.1007/978-3-031-89284-4 035 $a(CKB)38859191400041 035 $a(DE-He213)978-3-031-89284-4 035 $a(MiAaPQ)EBC32123558 035 $a(Au-PeEL)EBL32123558 035 $a(OCoLC)1524423986 035 $a(EXLCZ)9938859191400041 100 $a20250518d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Metaheuristics: Novel Approaches for Complex Problem Solving /$fby Erik Cuevas, Nahum Aguirre, Oscar Barba-Toscano, Mario Vásquez-Franco 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XVI, 228 p. 54 illus., 26 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v1210 311 08$a3-031-89283-6 327 $aOptimization -- Metaheuristic Algorithms -- Population initialization for metaheuristic algorithm based on the Gibbs sampling methodology -- Metaheuristic optimization with dynamic strategy adaptation -- Harnessing Locust Swarm Dynamics for Optimization Algorithms -- Diversity-Opposition hybridization of the Cheetah Optimizer for global optimization. 330 $aThis book examines a series of strategies designed to enhance metaheuristic algorithms, focusing on critical aspects such as initialization methods, the incorporation of Evolutionary Game Theory to develop novel search mechanisms, and the application of learning concepts to refine evolutionary operators. Furthermore, it emphasizes the significance of diversity and opposition in preventing premature convergence and improving algorithmic efficiency. These strategies collectively contribute to the development of more adaptive and robust optimization techniques. The book was designed from a teaching standpoint, making it suitable for undergraduate and postgraduate students in Science, Electrical Engineering, or Computational Mathematics. Furthermore, engineering practitioners unfamiliar with metaheuristic computations will find value in the application of these techniques to address complex real-world engineering problems, extending beyond theoretical constructs. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v1210 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$4aut$4http://id.loc.gov/vocabulary/relators/aut$0761169 702 $aAguirre$b Nahum$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aBarba-Toscano$b Oscar$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aVásquez-Franco$b Mario$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911003590403321 996 $aAdvanced Metaheuristics: Novel Approaches for Complex Problem Solving$94384252 997 $aUNINA