Parallel Metaheuristics: A New Class of Algorithms

Parallel Metaheuristics: A New Class of Algorithms

Editor(s): Enrique Alba

Published Online: 16 SEP 2005

Print ISBN: 9780471678069

Online ISBN: 9780471739388

DOI: 10.1002/0471739383

About this Book

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 contents

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  1. Part 1: Part I INTRODUCTION TO METAHEURISTICS AND PARALLELISM

  2. Part 2: Part II PARALLEL METAHEURISTIC MODELS

    1. Chapter 5

      Parallel Genetic Algorithms (pages 105–125)

      Gabriel Luque, Enrique Alba and Bernabé Dorronsoro

    1. Chapter 6

      Parallel Genetic Programming (pages 127–153)

      Francisco Fernández, Giandomenico Spezzano, Marco Tomassini and Leonardo Vanneschi

    1. Chapter 8

      Parallel Ant Colony Algorithms (pages 171–201)

      Stefan Janson, Daniel Merkle and Martin Middendorf

    1. Chapter 10

      Parallel Scatter Search (pages 223–246)

      Félix García López, Miguel García Torres, Belén Melián Batista, José A. Moreno Perez and J. Marcos Moreno Vega

    1. Chapter 11

      Parallel Variable Neighborhood Search (pages 247–266)

      José A. Moreno Pérez, Pierre Hansen and Nenad Mladenović

    1. Chapter 13

      Parallel Tabu Search (pages 289–313)

      Teodor Gabriel Crainic, Michel Gendreau and Jean- Yves Potvin

    1. Chapter 15

      Parallel Hybrid Metaheuristics (pages 347–370)

      Carlos Cotta, El-Ghazali Talbi and Enrique Alba

  3. Part 3: Part III THEORY AND APPLICATIONS

    1. Chapter 20

      Parallel Metaheuristics in Telecommunications (pages 495–515)

      Sergio Nesmachnow, Héctor Cancela, Enrique Alba and Francisco Chicano

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