【Springer 最优化】Optimization by GRASP (2016)

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Optimization by GRASP
Greedy Randomized Adaptive Search Procedures

Authors: Mauricio G.C. Resende, Celso C. Ribeiro



First book on GRASP, an optimization technique that has been used all over the world by engineers, optimization specialists, scientists, and more

Includes a how-to guide on designing efficient and effective GRASP heuristics to solve real-world optimization problems

Includes an original and innovative introduction to combinatorial optimization and heuristics

Based on extensive experience teaching GRASP and other metaheuristics to undergraduate and graduate students globally

Includes a section on case studies describing successful applications of GRASP in practice

Explores tools for performance evaluation and algorithm comparison which are not explored in other books in the area

Unified algorithm presentation based on pseudo-codes following the same style throughout the text

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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Optimization by GRASP
Greedy Randomized Adaptive Search Procedures

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Optimization by GRASP

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