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Essays and Surveys in Metaheuristics

Lingua IngleseInglese
Libro Rigido
Libro Essays and Surveys in Metaheuristics Celso C. Ribeiro
Codice Libristo: 01397096
Casa editrice Springer, gennaio 2001
The field of metaheuristics has been fast evolving in recent years. Techniques such as simulated ann... Descrizione completa
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The field of metaheuristics has been fast evolving in recent years. Techniques such as simulated annealing, tabu search, genetic algorithms, scatter search, greedy randomized adaptive search, variable neighborhood search, ant systems, and their hybrids are currently among the most efficient and robust optimization strategies to find high-quality solutions to many real-life optimization problems. A very large number of successful applications of metaheuristics are reported in the literature and spread throughout many books, journals, and conference proceedings. A series of international conferences entirely devoted to the theory, applications, and computational developments in metaheuristics has been attracting an increasing number of participants, from universities and the industry. Essays and Surveys in Metaheuristics goes beyond the recent conference-oriented volumes in Metaheuristics, with its focus on surveys of recent developments of the main metaheuristics. Well-known specialists have written surveys on the following subjects: simulated annealing (E. Aarts and J. Korst, The Netherlands), noising methods (I. Charon and O. Hudry, France), strategies for the parallel implementation of metaheuristics (V.-D. Cung and C. Roucairol, France, and S.L. Martins and C.C. Ribeiro, Brazil), greedy randomized adaptive search procedures (P. Festa, Italy, and M.G.C. Resende, USA), tabu search (M. Gendreau, Canada), variable neighborhood search (P. Hansen and N. Mladenovic, Canada), ant colonies (V. Maniezzo and A. Carbonaro, Italy), and evolutionary algorithms (H. Mühlenbein and Th. Mahnig, Germany). Several further essays address issues or variants of metaheuristics, as well as innovative or successful applications of metaheuristics to classical or new combinatorial optimization problems.

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