Applied Metaheuristic Computing

• Autore: Yin Peng-Yeng
• 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
• 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