Optimization by Grasp: Greedy Randomized Adaptive Search Procedures

ISBN-13: 9781493965281 / Angielski / Twarda / 2016 / 312 str.

Mauricio Resende; Celso C. Ribeiro

Opis

GRASP (Greedy Randomized Adaptive Search Procedures) is a metaheuristic applied to real-world combinatorial optimization problems. This book introduces GRASP and combinatorial optimization, greedy algorithms, local search and path-relinking, with a focus on algorithmic and computational aspects. It includes a substantial bibliography.

Recenzje

"Optimization by GRASP is a well-structured and well written introduction to GRASP. In addition it is very suitable for and highly accessible to students, researchers and practitioners who want to familiarize themselves with combinatorial optimization and greedy algorithms. ... The book provides an excellent overview of GRASP and will appeal to researchers and practitioners of combinatorial optimization." (Hans W. Ittmann, IFORS News, Vol. 12 (01), March, 2018)

"The book is a comprehensive introduction to the greedy randomized adaptive search procedures (GRASP), first applied to the set covering problems and then to other combinatorial problems. ... The book is a very good choice for scientists, students and engineers, introducing to the subject of GRASP ... . I strongly recommend this book to both theoreticians and practitionners of OR." (Marcin Anholcer, zbMATH 1356.90001, 2017)

Spis treści

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.

Blurb

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.