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Advances in Metaheuristics for Hard Optimization

  • Book
  • © 2008

Overview

Editors:
  1. Patrick Siarry

    1. Laboratory LiSSi, University of Paris 12, Créteil, France

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    You can also search for this editor in PubMed   Google Scholar

  2. Zbigniew Michalewicz

    1. School of Computer Science, University of Adelaide, Adelaide, Australia

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Natural Computing Series (NCS)

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Table of contents (21 chapters)

  1. Front Matter

    Pages I-XV
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  2. Comparison of Simulated Annealing, Interval Partitioning and Hybrid Algorithms in Constrained Global Optimization

    • Chandra Sekhar Pedamallu, Linet Özdamar
    Pages 1-22

  3. Four-bar Mechanism Synthesis for n Desired Path Points Using Simulated Annealing

    • Horacio Martínez-Alfaro
    Pages 23-37

  4. “MOSS-II” Tabu/Scatter Search for Nonlinear Multiobjective Optimization

    • Ricardo P. Beausoleil
    Pages 39-67

  5. Feature Selection for Heterogeneous Ensembles of Nearest-neighbour Classifiers Using Hybrid Tabu Search

    • Muhammad A. Tahir, James E. Smith
    Pages 69-85

  6. A Parallel Ant Colony Optimization Algorithm Based on Crossover Operation

    • Adem Kalinli, Fatih Sarikoc
    Pages 87-110

  7. An Ant-bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions

    • Alberto V. Donati, Vince Darley, Bala Ramachandran
    Pages 111-136

  8. Dynamic Load Balancing Using an Ant Colony Approach in Micro-cellular Mobile Communications Systems

    • Sung-Soo Kim, Alice E. Smith, Soon-Jung Hong
    Pages 137-152

  9. New Ways to Calibrate Evolutionary Algorithms

    • Gusz Eiben, Martijn C. Schut
    Pages 153-177

  10. Divide-and-Evolve: a Sequential Hybridization Strategy Using Evolutionary Algorithms

    • Marc Schoenauer, Pierre Savéant, Vincent Vidal
    Pages 179-198

  11. Local Search Based on Genetic Algorithms

    • Carlos García-Martínez, Manuel Lozano
    Pages 199-221

  12. Designing Efficient Evolutionary Algorithms for Cluster Optimization: A Study on Locality

    • Francisco B. Pereira, JorgeM.C. Marques, Tiago Leitão, Jorge Tavares
    Pages 223-250

  13. Aligning Time Series with Genetically Tuned Dynamic Time Warping Algorithm

    • Pankaj Kumar, Ankur Gupta, Valadi K. Jayaraman, BhaskarD. Kulkarni
    Pages 251-261

  14. Evolutionary Generation of Artificial Creature’s Personality for Ubiquitous Services

    • Jong-Hwan Kim, Chi-Ho Lee, Kang-Hee Lee, Naveen S. Kuppuswamy
    Pages 263-292

  15. Some Guidelines for Genetic Algorithm Implementation in MINLP Batch Plant Design Problems

    • Antonin Ponsich, Catherine Azzaro-Pantel, Serge Domenech, Luc Pibouleau
    Pages 293-315

  16. Coevolutionary Genetic Algorithm to Solve Economic Dispatch

    • Márcia Marcondes Altimari Samed, Mauro Antonio da Silva Sa Ravagnani
    Pages 317-327

  17. An Evolutionary Approach to Solve a Novel Mechatronic Multiobjective Optimization Problem

    • Efrén Mezura-Montes, Edgar A. Portilla-Flores, Carlos A. Coello Coello, Jaime Alvarez-Gallegos, Carlos A. Cruz-Villar
    Pages 329-351

  18. Optimizing Stochastic Functions Using a Genetic Algorithm: An Aeronautic Military Application

    • Hélcio Vieira
    Pages 353-363

  19. Learning Structure Illuminates Black Boxes – An Introduction to Estimation of Distribution Algorithms

    • Jörn Grahl, Stefan Minner, Peter A.N. Bosman
    Pages 365-395

  20. Making a Difference to Differential Evolution

    • Zhenyu Yang, Xin Yao, Jingsong He
    Pages 397-414
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Keywords

About this book

Many advances have been made recently in metaheuristic methods, from theory to applications. The editors, both leading experts in this field, have assembled a team of researchers to contribute 21 chapters organized into parts on simulated annealing, tabu search, ant colony algorithms, general-purpose studies of evolutionary algorithms, applications of evolutionary algorithms, and various metaheuristics.

The book gathers contributions related to the following topics: theoretical developments in metaheuristics; adaptation of discrete metaheuristics to continuous optimization; performance comparisons of metaheuristics; cooperative methods combining different approaches; parallel and distributed metaheuristics for multiobjective optimization; software implementations; and real-world applications.

This book is suitable for practitioners, researchers and graduate students in disciplines such as optimization, heuristics, operations research, and natural computing.

Editors and Affiliations

  • Laboratory LiSSi, University of Paris 12, Créteil, France

    Patrick Siarry

  • School of Computer Science, University of Adelaide, Adelaide, Australia

    Zbigniew Michalewicz

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