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Heuristics for Optimization and Learning

  • Book
  • © 2021

Overview

Editors:
  1. Farouk Yalaoui

    1. Laboratory of Logistics and Optimization of Industrial Systems (LOSI), University of Technology of Troyes (UTT), Troyes Cedex, France

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  2. Lionel Amodeo

    1. Industrial System Optimization Laboratory, University of Technology of Troyes (UTT), Troyes Cedex, France

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  3. El-Ghazali Talbi

    1. CRISTAL UMR CNRS 9189 & INRIA Lille Nord Europe, Parc Scientifique de la Haute Borne, Polytech'Lille - Univrsité of Lille, Villeneuve d'Ascq, France

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  • Presents recent research on Heuristics for Optimization and Learning
  • Gathers contributions in optimization technique for production decision, general development for optimization and computing methods, and wider spread applications
  • Edited results of the 7th International Conference on Metaheuristics and Nature Inspired Computing META’18, held at Marrakech, Morocco October 27–31, 2018
  • Written by experts in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 906)

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Table of contents (28 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Process Plan Generation for Reconfigurable Manufacturing Systems: Exact Versus Evolutionary-Based Multi-objective Approaches

    • Faycal A. Touzout, Hichem Haddou Benderbal, Amirhossein Khezri, Lyes Benyoucef
    Pages 1-15

  3. On VNS-GRASP and Iterated Greedy Metaheuristics for Solving Hybrid Flow Shop Scheduling Problem with Uniform Parallel Machines and Sequence Independent Setup Time

    • Said Aqil, Karam Allali
    Pages 17-32

  4. A Variable Block Insertion Heuristic for the Energy-Efficient Permutation Flowshop Scheduling with Makespan Criterion

    • M. Fatih Tasgetiren, Hande Oztop, Quan-Ke Pan, M. Arslan Ornek, Talya Temizceri
    Pages 33-49

  5. Solving 0-1 Bi-Objective Multi-dimensional Knapsack Problems Using Binary Genetic Algorithm

    • Ozgur Kabadurmus, M. Fatih Tasgetiren, Hande Oztop, M. Serdar Erdogan
    Pages 51-67

  6. An Asynchronous Parallel Evolutionary Algorithm for Solving Large Instances of the Multi-objective QAP

    • Florian Mazière, Pierre Delisle, Caroline Gagné, Michaël Krajecki
    Pages 69-85

  7. Learning from Prior Designs for Facility Layout Optimization

    • Hannu Rummukainen, Jukka K. Nurminen, Timo Syrjänen, Jukka-Pekka Numminen
    Pages 87-101

  8. Single-Objective Real-Parameter Optimization: Enhanced LSHADE-SPACMA Algorithm

    • Anas A. Hadi, Ali W. Mohamed, Kamal M. Jambi
    Pages 103-121

  9. Operations Research at Bulk Terminal: A Parallel Column Generation Approach

    • Gustavo Campos Menezes, Lucas Teodoro de Lima Santos, João Fernando Machry Sarubbi, Geraldo Robson Mateus
    Pages 123-137

  10. Heuristic Solutions for the \({(\alpha , \beta )}\)-k Feature Set Problem

    • Leila M. Naeni, Amir Salehipour
    Pages 139-150

  11. Generic Support for Precomputation-Based Global Routing Constraints in Local Search Optimization

    • Renaud De Landtsheer, Fabian Germeau, Thomas Fayolle, Gustavo Ospina, Christophe Ponsard
    Pages 151-165

  12. Dynamic Simulated Annealing with Adaptive Neighborhood Using Hidden Markov Model

    • Mohamed Lalaoui, Abdellatif El Afia, Raddouane Chiheb
    Pages 167-182

  13. Hybridization of the Differential Evolution Algorithm for Continuous Multi-objective Optimization

    • Caroline Gagné, Aymen Sioud, Marc Gravel, Mathieu Fournier
    Pages 183-198

  14. A Steganographic Embedding Scheme Using Improved-PSO Approach

    • Yamina Mohamed Ben Ali
    Pages 199-210

  15. Algorithms Towards the Automated Customer Inquiry Classification

    • Gulshat Kessikbayeva, Nazerke Sultanova, Yerbolat Amangeldi, Roman Yurchenko
    Pages 211-222

  16. An Heuristic Scheme for a Reaction Advection Diffusion Equation

    • M. R. Amattouch, H. Belhadj
    Pages 223-238

  17. Stock Market Speculation System Development Based on Technico Temporal Indicators and Data Mining Tools

    • Zineb Bousbaa, Omar Bencharef, Abdellah Nabaji
    Pages 239-251

  18. A New Hidden Markov Model Approach for Pheromone Level Exponent Adaptation in Ant Colony System

    • Safae Bouzbita, Abdellatif El Afia, Rdouan Faizi
    Pages 253-267

  19. A New Cut-Based Genetic Algorithm for Graph Partitioning Applied to Cell Formation

    • Menouar Boulif
    Pages 269-284

  20. Memetic Algorithm and Evolutionary Operators for Multi-Objective Matrix Tri-Factorization Problem

    • Rok Hribar, Gašper Petelin, Jurij Šilc, Gregor Papa, Vida Vukašinović
    Pages 285-298
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Keywords

About this book

This book is a new contribution aiming to give some last research findings in the field of optimization and computing. This work is in the same field target than our two previous books published: “Recent Developments in Metaheuristics” and “Metaheuristics for Production Systems”, books in Springer Series in Operations Research/Computer Science Interfaces.

The challenge with this work is to gather the main contribution in three fields, optimization technique for production decision, general development for optimization and computing method and wider spread applications. 

The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields.

Editors and Affiliations

  • Laboratory of Logistics and Optimization of Industrial Systems (LOSI), University of Technology of Troyes (UTT), Troyes Cedex, France

    Farouk Yalaoui

  • Industrial System Optimization Laboratory, University of Technology of Troyes (UTT), Troyes Cedex, France

    Lionel Amodeo

  • CRISTAL UMR CNRS 9189 & INRIA Lille Nord Europe, Parc Scientifique de la Haute Borne, Polytech'Lille - Univrsité of Lille, Villeneuve d'Ascq, France

    El-Ghazali Talbi

Bibliographic Information

  • Book Title: Heuristics for Optimization and Learning

  • Editors: Farouk Yalaoui, Lionel Amodeo, El-Ghazali Talbi

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-030-58930-1

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

  • Hardcover ISBN: 978-3-030-58929-5Published: 16 December 2020

  • Softcover ISBN: 978-3-030-58932-5Published: 17 December 2021

  • eBook ISBN: 978-3-030-58930-1Published: 15 December 2020

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XV, 442

  • Number of Illustrations: 71 b/w illustrations, 97 illustrations in colour

  • Topics: Computational Intelligence, Optimization, Machine Learning

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