Solving complex optimization problems with parallel
metaheuristics
Parallel Metaheuristics brings together an international group of
experts in parallelism and metaheuristics to provide a much-needed
synthesis of these two fields. Readers discover how metaheuristic
techniques can provide useful and practical solutions for a wide
range of problems and application domains, with an emphasis on the
fields of telecommunications and bioinformatics. This volume fills
a long-existing gap, allowing researchers and practitioners to
develop efficient metaheuristic algorithms to find solutions.
The book is divided into three parts:
* Part One: Introduction to Metaheuristics and Parallelism,
including an Introduction to Metaheuristic Techniques, Measuring
the Performance of Parallel Metaheuristics, New Technologies in
Parallelism, and a head-to-head discussion on Metaheuristics and
Parallelism
* Part Two: Parallel Metaheuristic Models, including Parallel
Genetic Algorithms, Parallel Genetic Programming, Parallel
Evolution Strategies, Parallel Ant Colony Algorithms, Parallel
Estimation of Distribution Algorithms, Parallel Scatter Search,
Parallel Variable Neighborhood Search, Parallel Simulated
Annealing, Parallel Tabu Search, Parallel GRASP, Parallel Hybrid
Metaheuristics, Parallel Multi-Objective Optimization, and Parallel
Heterogeneous Metaheuristics
* Part Three: Theory and Applications, including Theory of Parallel
Genetic Algorithms, Parallel Metaheuristics Applications, Parallel
Metaheuristics in Telecommunications, and a final chapter on
Bioinformatics and Parallel Metaheuristics
Each self-contained chapter begins with clear overviews and
introductions that bring the reader up to speed, describes basic
techniques, and ends with a reference list for further study.
Packed with numerous tables and figures to illustrate the complex
theory and processes, this comprehensive volume also includes
numerous practical real-world optimization problems and their
solutions.
This is essential reading for students and researchers in computer
science, mathematics, and engineering who deal with parallelism,
metaheuristics, and optimization in general.
Table of Content
Foreword.
Preface
Contributors.
PART I: INTRODUCTION TO METAHEURISITICS AND PARALLELISM.
1. An Introduction to Metaheuristic Techniques (C. Blum, et
al.).
2. Measuring the Performance of Parallel Metaheuristics (E. Alba
& G. Luque).
3. New Technologies in Parallelism (E. Alba & A. Nebro).
4. Metaheuristics and Parallelism (E. Alba, et al.).
PART II: PARALLEL METAHEURISTIC MODELS.
5. Parallel Genetic Algorithms (G. Luque, et al.).
6. Parallel Genetic Programming (F. Fernández, et al.).
7. Parallel Evolution Strategies (G. Rudolph).
8. Parallel Ant Colony Algorithms (S. Janson, et al.).
9. Parallel Estimation of Distribution Algorithms (J. Madera, et
al.).
10. Parallel Scatter Search (F. Garcia, et al.).
11. Parallel Variable Neighborhood Search (J. Moreno-Pérez,
et al.).
12. Parallel Simulated Annealing (M. Aydin, V. Yigit).
13. Parallel Tabu Search (T. Crainic, et al.).
14. Parallel Greedy Randomized Adaptive Search Procedures (M.
Resende & C. Ribeiro).
15. Parallel Hybrid Metaheuristics (C. Cotta, et al.).
16. Parallel Multi Objective Optimization (A. Nebro, et al.).
17. Parallel Heterogeneous Metaheuristics (F. Luna, et al.).
PART III: THEORY AND APPLICATIONS.
18. Theory of Parallel Genetic Algorithms (E.
Cantú-Paz).
19. Parallel Metaheuristics Applications (T. Crainic & N.
Hail).
20. Parallel Metaheuristics in Telecommunications (S.
Nesmachnow, et al.).
21. Bioinformatics and Parallel Metaheuristics (O. Trelles, A.
Rodriguez).
Index.
About the author
ENRIQUE ALBA, Ph D, is a Professor of Computer Science at the University of Málaga, Spain. His research interests involve the design and application of evolutionary algorithms, neural networks, parallelism, and metaheuristic algorithms to solve problems in telecommunications, combinatorial optimization, and bioinformatics. Dr. Alba has published many papers in leading journals and international conferences, and has garnered international awards for his research.
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