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Optimization by GRASP = greedy rando...

Resende, Mauricio G.C.

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Optimization by GRASP = greedy randomized adaptive search procedures /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimization by GRASP/ by Mauricio G.C. Resende, Celso C. Ribeiro.
其他題名:	greedy randomized adaptive search procedures /
作者:	Resende, Mauricio G.C.
其他作者:	Ribeiro, Celso C.
出版者:	New York, NY :Springer New York : : 2016.,
面頁冊數:	xx, 312 p. :ill. (some col.), digital ;24 cm.
內容註:	Foreword -- Preface -- 1. Introduction -- 2. A short tour of combinatorial optimization and computational complexity -- 3. Solution construction and greedy algorithms -- 4. Local search -- 5. GRASP: The basic heuristic -- 6. Runtime distributions -- 7. GRASP: extended construction heuristics -- 8. Path-relinking -- 9. GRASP with Path-relinking -- 10. Parallel GRASP heuristics -- 11. GRASP for continuous optimization -- 12. Case studies -- References -- Index.
Contained By:	Springer eBooks
標題:	Mathematical optimization. -
電子資源:	http://dx.doi.org/10.1007/978-1-4939-6530-4
ISBN:	9781493965304

Optimization by GRASP = greedy randomized adaptive search procedures /
Resende, Mauricio G.C.

Optimization by GRASPgreedy randomized adaptive search procedures /[electronic resource] :by Mauricio G.C. Resende, Celso C. Ribeiro. - New York, NY :Springer New York :2016. - xx, 312 p. :ill. (some col.), digital ;24 cm.

Foreword -- Preface -- 1. Introduction -- 2. A short tour of combinatorial optimization and computational complexity -- 3. Solution construction and greedy algorithms -- 4. Local search -- 5. GRASP: The basic heuristic -- 6. Runtime distributions -- 7. GRASP: extended construction heuristics -- 8. Path-relinking -- 9. GRASP with Path-relinking -- 10. Parallel GRASP heuristics -- 11. GRASP for continuous optimization -- 12. Case studies -- References -- Index.

This is the first book to cover GRASP (Greedy Randomized Adaptive Search Procedures), a metaheuristic that has enjoyed wide success in practice with a broad range of applications to real-world combinatorial optimization problems. The state-of-the-art coverage and carefully crafted pedagogical style lends this book highly accessible as an introductory text not only to GRASP, but also to combinatorial optimization, greedy algorithms, local search, and path-relinking, as well as to heuristics and metaheuristics, in general. The focus is on algorithmic and computational aspects of applied optimization with GRASP with emphasis given to the end-user, providing sufficient information on the broad spectrum of advances in applied optimization with GRASP. For the more advanced reader, chapters on hybridization with path-relinking and parallel and continuous GRASP present these topics in a clear and concise fashion. Additionally, the book offers a very complete annotated bibliography of GRASP and combinatorial optimization. For the practitioner who needs to solve combinatorial optimization problems, the book provides a chapter with four case studies and implementable templates for all algorithms covered in the text. This book, with its excellent overview of GRASP, will appeal to researchers and practitioners of combinatorial optimization who have a need to find optimal or near optimal solutions to hard combinatorial optimization problems.

ISBN: 9781493965304

Standard No.: 10.1007/978-1-4939-6530-4doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

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520 $a This is the first book to cover GRASP (Greedy Randomized Adaptive Search Procedures), a metaheuristic that has enjoyed wide success in practice with a broad range of applications to real-world combinatorial optimization problems. The state-of-the-art coverage and carefully crafted pedagogical style lends this book highly accessible as an introductory text not only to GRASP, but also to combinatorial optimization, greedy algorithms, local search, and path-relinking, as well as to heuristics and metaheuristics, in general. The focus is on algorithmic and computational aspects of applied optimization with GRASP with emphasis given to the end-user, providing sufficient information on the broad spectrum of advances in applied optimization with GRASP. For the more advanced reader, chapters on hybridization with path-relinking and parallel and continuous GRASP present these topics in a clear and concise fashion. Additionally, the book offers a very complete annotated bibliography of GRASP and combinatorial optimization. For the practitioner who needs to solve combinatorial optimization problems, the book provides a chapter with four case studies and implementable templates for all algorithms covered in the text. This book, with its excellent overview of GRASP, will appeal to researchers and practitioners of combinatorial optimization who have a need to find optimal or near optimal solutions to hard combinatorial optimization problems.
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