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Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics

International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007, Proceedings

  • Conference proceedings
  • © 2007

Overview

Editors:
  1. Thomas Stützle
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  2. Mauro Birattari
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  3. Holger H. Hoos
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4638)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: SLS 2007.

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Table of contents (21 papers)

  1. Front Matter

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  2. The Importance of Being Careful

    1. The Importance of Being Careful

      • Arne Løkketangen
      Pages 1-15

    2. Designing and Tuning SLS Through Animation and Graphics: An Extended Walk-Through

      • Steven Halim, Roland H. C. Yap
      Pages 16-30

    3. Implementation Effort and Performance

      • Paola Pellegrini, Mauro Birattari
      Pages 31-45

    4. Tuning the Performance of the MMAS Heuristic

      • Enda Ridge, Daniel Kudenko
      Pages 46-60

    5. Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions

      • Frank Neumann, Dirk Sudholt, Carsten Witt
      Pages 61-75

    6. EasyAnalyzer: An Object-Oriented Framework for the Experimental Analysis of Stochastic Local Search Algorithms

      • Luca Di Gaspero, Andrea Roli, Andrea Schaerf
      Pages 76-90

    7. Mixed Models for the Analysis of Local Search Components

      • Jørgen Bang-Jensen, Marco Chiarandini, Yuri Goegebeur, Bent Jørgensen
      Pages 91-105

    8. An Algorithm Portfolio for the Sub-graph Isomorphism Problem

      • Roberto Battiti, Franco Mascia
      Pages 106-120

    9. A Path Relinking Approach for the Multi-Resource Generalized Quadratic Assignment Problem

      • Mutsunori Yagiura, Akira Komiya, Kenya Kojima, Koji Nonobe, Hiroshi Nagamochi, Toshihide Ibaraki et al.
      Pages 121-135

    10. A Practical Solution Using Simulated Annealing for General Routing Problems with Nodes, Edges, and Arcs

      • Hisafumi Kokubugata, Ayako Moriyama, Hironao Kawashima
      Pages 136-149

    11. Probabilistic Beam Search for the Longest Common Subsequence Problem

      • Christian Blum, Maria J. Blesa
      Pages 150-161

    12. A Bidirectional Greedy Heuristic for the Subspace Selection Problem

      • Dag Haugland
      Pages 162-176

  3. Short Papers

    1. EasySyn++: A Tool for Automatic Synthesis of Stochastic Local Search Algorithms

      • Luca Di Gaspero, Andrea Schaerf
      Pages 177-181

    2. Human-Guided Enhancement of a Stochastic Local Search: Visualization and Adjustment of 3D Pheromone

      • Jaya Sreevalsan-Nair, Meike Verhoeven, David L. Woodruff, Ingrid Hotz, Bernd Hamann
      Pages 182-186

    3. Solving a Bi-objective Vehicle Routing Problem by Pareto-Ant Colony Optimization

      • Joseph M. Pasia, Karl F. Doerner, Richard F. Hartl, Marc Reimann
      Pages 187-191

    4. A Set Covering Approach for the Pickup and Delivery Problem with General Constraints on Each Route

      • Hideki Hashimoto, Youichi Ezaki, Mutsunori Yagiura, Koji Nonobe, Toshihide Ibaraki, Arne Løkketangen
      Pages 192-196

    5. A Study of Neighborhood Structures for the Multiple Depot Vehicle Scheduling Problem

      • Benoît Laurent, Jin-Kao Hao
      Pages 197-201

    6. Local Search in Complex Scheduling Problems

      • Thijs Urlings, Rubén Ruiz
      Pages 202-206

    7. A Multi-sphere Scheme for 2D and 3D Packing Problems

      • Takashi Imamichi, Hiroshi Nagamochi
      Pages 207-211
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  1. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics

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About this book

Stochastic local search (SLS) algorithms enjoy great popularity as powerful and versatile tools for tackling computationally hard decision and optimization pr- lems from many areas of computer science, operations research, and engineering. To a large degree, this popularity is based on the conceptual simplicity of many SLS methods and on their excellent performance on a wide gamut of problems, ranging from rather abstract problems of high academic interest to the very s- ci?c problems encountered in many real-world applications. SLS methods range from quite simple construction procedures and iterative improvement algorithms to more complex general-purpose schemes, also widely known as metaheuristics, such as ant colony optimization, evolutionary computation, iterated local search, memetic algorithms, simulated annealing, tabu search and variable neighborhood search. Historically, the development of e?ective SLS algorithms has been guided to a large extent by experience and intuition, and overall resembled more an art than a science. However, in recent years it has become evident that at the core of this development task there is a highly complex engineering process, which combines various aspects of algorithm design with empirical analysis techniques and problem-speci?c background, and which relies heavily on knowledge from a number of disciplines and areas, including computer science, operations research, arti?cial intelligence, and statistics. This development process needs to be - sisted by a sound methodology that addresses the issues arising in the various phases of algorithm design, implementation, tuning, and experimental eval- tion.

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