Discrete Diversity and Dispersion Maximization
Language: English
Published by Springer International Publishing, 2023
- Hardcover
- New
Condition: New
£ 110.87
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Basic principles and fundamental ideas of successful methodologies for discrete optimizationAllows the readers to create their own applications to other discrete optimization problems through tutorialsAdvances in heuristic optimization, foc.
Seller Inventory # 892114200
- Title
- Discrete Diversity and Dispersion Maximization
- Publisher
- Springer International Publishing
- Publication year
- 2023
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3031383095
- ISBN 13
- 9783031383090
This book demonstrates the metaheuristic methodologies that apply to maximum diversity problems to solve them. Maximum diversity problems arise in many practical settings from facility location to social network analysis and constitute an important class of NP-hard problems in combinatorial optimization. In fact, this volume presents a “missing link” in the combinatorial optimization-related literature. In providing the basic principles and fundamental ideas of the most successful methodologies for discrete optimization, this book allows readers to create their own applications for other discrete optimization problems. Additionally, the book is designed to be useful and accessible to researchers and practitioners in management science, industrial engineering, economics, and computer science, while also extending value to non-experts in combinatorial optimization. Owed to the tutorials presented in each chapter, this book may be used in a master course, a doctoral seminar, or as supplementary to a primary text in upper undergraduate courses.
The chapters are divided into three main sections. The first section describes a metaheuristic methodology in a tutorial style, offering generic descriptions that, when applied, create an implementation of the methodology for any optimization problem. The second section presents the customization of the methodology to a given diversity problem, showing how to go from theory to application in creating a heuristic. The final part of the chapters is devoted to experimentation, describing the results obtained with the heuristic when solving the diversity problem. Experiments in the book target the so-called MDPLIB set of instances as a benchmark to evaluate the performance of the methods.
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