Books and Volumes
This page contains information about the five volumes that I have done with other colleagues. Two of them are monographies, another two are edited volumes with many authors, and one of them is a journal special issue. They cannot be directly downloadable here. If you are interested in any of them, please contact the editorial office (follow the links below) or your book supplier.This handbook constitutes an important effort to put together all the state-of-the-art methodologies in the field of heuristic optimization. It has three volumes with close to 50 chapters classified in 5 sections: Search strategies, Local search, metaheuristics, analysis and implementations, and Applications. It is in its last stage of production and will be pubished by Springer in 2017. Metaheuristics in Business Analytics
A. Duarte, M. Laguna, and R. Martí,
EURO Advanced Tutorials on Operations Research
This essential metaheuristics tutorial provides descriptions and practical applications in the area of business analytics. It addresses key problems in predictive and prescriptive analysis, while also illustrating how problems that arise in business analytics can be modelled and how metaheuristics can be used to find high-quality solutions. Readers will be introduced to decision-making problems for which metaheuristics offer the most effective solution technique. The book not only shows business problem modelling on a spreadsheet but also how to design and create a Visual Basic for Applications code.
Scatter Search: Methodology and Implementations in C
M. Laguna and R. Martí,
Kluwer Academic Publishers, Boston
Operations Research/Computer Science Interfaces Series
ISBN 1-4020-7376-3, 312 pp. (2003)
The evolutionary approach called scatter search originated from strategies for creating composite decision rules and surrogate constraints. Recent studies demonstrate the practical advantages of this approach for solving a diverse array of optimization problems from both classical and real world settings. Scatter search contrasts with other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on generalized path constructions in Euclidean space and by utilizing strategic designs where other approaches resort to randomization. The book's goal is to provide the basic principles and fundamental ideas that will allow the readers to create successful applications of scatter search. The book includes the C source code of the methods introduced in each chapter.
Scatter Search Methods for Optimization
Rafael Martí (Guest Editor).
A feature issue of the European Journal of Operational Research
169, 2(1), March 2006 The papers in this volume cover a wide range of SS and PR implementations. Regarding complexity, we can find basic designs as well as advanced implementations, while from a hybridization viewpoint we can find “pure” SS designs and hybrid algorithms, in which the SS elements are combined with other metaheuristics, such as tabu search or GAs. We have grouped the papers in this volume into the following categories:
Foundations, Nonlinear Optimization, Optimization in Graphs, Parallel Optimization, Prediction and Clustering, Routing, and Scheduling.
Metaheuristics for Training Neural Networks
Enrique Alba and Rafael Martí (Editors),
Springer Science, New York
ISBN 0-387-33415-7, 252 pages, 2006 The book’s goal is to provide successful implementations of metaheuristic methods for neural network training. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own. Apart from Chapter 1, in which classical training methods are reviewed for the sake of the book’s completeness, we have classified the chapters in three main categories. The first one is devoted to local search based methods, in which we include Simulated Annealing, Tabu Search, and Variable Neighborhood Search. The second part of the book presents the most effective population based methods, such as Estimation Distribution algorithms, Scatter Search, and Genetic Algorithms. Finally, the third part includes other advanced techniques, such as Ant Colony Optimization, Co-evolutionary methods, GRASP, and Memetic algorithms. All these methods have been shown to work out high quality solutions in a wide range of hard optimization problems, while in this book we restrict our attention to their application to the ANN training problem.
The Linear Ordering Problem. Exact and heuristic methods in combinatorial optimization
Rafael Martí and Gerhard Reinelt
ISBN: 978-3-642-16728-7, 171 pages, (2011) This monograph sets out state-of-the-art optimization methods for tackling the ‘linear ordering problem’ (LOP). Whereas important applications in business, engineering and economics lie beyond the reach of methodologies that have been the focus of academic research for three decades, the fresh approaches set out in this volume provide practical solutions to the LOP. The focus on the LOP does not limit the monograph’s scope and applicability, however. The exact and heuristic techniques outlined in these pages can be put to use in any number of combinatorial optimization problems. While the authors employ the LOP to illustrate cutting-edge optimization technologies, the book is also a tutorial on how to design effective and successful implementations of exact and heuristic procedures alike. The information in these pages provides readers with a toolkit that can be employed in a variety of settings. As a result, the book will be of great interest to researchers and practitioners in a number of fields, including computer science, mathematics, operations research, management science, industrial engineering, and economics. It is also suitable for use as a textbook on issues of practical optimization in a masters course, or as a reference book for engineering optimization algorithms. The authors have sought to make the book accessible to as wide an audience as possible by providing the reader with basic definitions and concepts in optimization.