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

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

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.

• AI/OR techniques
• Boolean function
• algorithm perfornamce
• algorithms
• behavior of SLS algorithms
• calculus
• dynamic behavior
• linear optimization
• metaheuristic
• methodological developments
• optimization
• programming
• search space analysis
• tuning procedures
• visualization
• data structures
• algorithm analysis and problem complexity

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