Hybrid Metaheuristics, Christian Blum; Carlos Cotta; Antonio Jos? Fern?nd

Автор: Christian Blum; Luca Gaspero; Andrea Roli; Michael
Название: Hybrid Metaheuristics
ISBN: 3642049176 ISBN-13(EAN): 9783642049170
Издательство: Springer
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Автор: Christian Blum; G?nther R. Raidl
Название: Hybrid Metaheuristics
ISBN: 3319308823 ISBN-13(EAN): 9783319308821
Издательство: Springer
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Автор: Maria J. Blesa; Christian Blum; Angelo Cangelosi;
Название: Hybrid Metaheuristics
ISBN: 3319396358 ISBN-13(EAN): 9783319396354
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Описание: This book constitutes the refereed proceedings of the 10th International Workshop on Hybrid Metaheuristics, HM 2016, held in Plymouth, UK, in June 2016. The 15 revised full papers presented were carefully reviewed and selected from 43 submissions.

Автор: Maria J. Blesa; Christian Blum; Stefan Vo?
Название: Hybrid Metaheuristics
ISBN: 3319076434 ISBN-13(EAN): 9783319076430
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Описание: This book constitutes the refereed proceedings of the 9th International Workshop on Hybrid Metaheuristics, HM 2014, held in Hamburg, Germany, in June 2014.

Автор: Mauricio G.C. Resende; J. Pinho de Sousa
Название: Metaheuristics
ISBN: 1441954031 ISBN-13(EAN): 9781441954039
Издательство: Springer
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Описание: Combinatorial optimization is the process of finding the best, or optimal, so- lution for problems with a discrete set of feasible solutions. Applications arise in numerous settings involving operations management and logistics, such as routing, scheduling, packing, inventory and production management, lo- cation, logic, and assignment of resources. The economic impact of combi- natorial optimization is profound, affecting sectors as diverse as transporta- tion (airlines, trucking, rail, and shipping), forestry, manufacturing, logistics, aerospace, energy (electrical power, petroleum, and natural gas), telecommu- nications, biotechnology, financial services, and agriculture. While much progress has been made in finding exact (provably optimal) so- lutions to some combinatorial optimization problems, using techniques such as dynamic programming, cutting planes, and branch and cut methods, many hard combinatorial problems are still not solved exactly and require good heuristic methods. Moreover, reaching "optimal solutions" is in many cases meaningless, as in practice we are often dealing with models that are rough simplifications of reality. The aim of heuristic methods for combinatorial op- timization is to quickly produce good-quality solutions, without necessarily providing any guarantee of solution quality. Metaheuristics are high level procedures that coordinate simple heuristics, such as local search, to find solu- tions that are of better quality than those found by the simple heuristics alone: Modem metaheuristics include simulated annealing, genetic algorithms, tabu search, GRASP, scatter search, ant colony optimization, variable neighborhood search, and their hybrids.