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Applied Metaheuristic Computing



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Autore: Yin Peng-Yeng Visualizza persona
Titolo: Applied Metaheuristic Computing Visualizza cluster
Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica: 1 electronic resource (684 p.)
Soggetto topico: Technology: general issues
History of engineering & technology
Soggetto non controllato: metaheuristics
heuristics
optimization
artificial intelligence
energy
information security
recognition
Persona (resp. second.): ChangRay-I
GheraibiaYoucef
ChuangMing-Chin
LinHua-Yi
LeeJen-Chun
YinPeng-Yeng
Sommario/riassunto: For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC.
Titolo autorizzato: Applied Metaheuristic Computing  Visualizza cluster
ISBN: 3-0365-5570-6
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910637780103321
Lo trovi qui: Univ. Federico II
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