Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics: International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007. Proceedings

Arne Løkketangen (auth.), Thomas Stützle, Mauro Birattari, Holger H. Hoos (eds.)3540744452, 9783540744450

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.

---

Table of contents :
Front Matter….Pages –
The Importance of Being Careful….Pages 1-15
Designing and Tuning SLS Through Animation and Graphics: An Extended Walk-Through….Pages 16-30
Implementation Effort and Performance….Pages 31-45
Tuning the Performance of the MMAS Heuristic….Pages 46-60
Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions….Pages 61-75
EasyAnalyzer: An Object-Oriented Framework for the Experimental Analysis of Stochastic Local Search Algorithms….Pages 76-90
Mixed Models for the Analysis of Local Search Components….Pages 91-105
An Algorithm Portfolio for the Sub-graph Isomorphism Problem….Pages 106-120
A Path Relinking Approach for the Multi-Resource Generalized Quadratic Assignment Problem….Pages 121-135
A Practical Solution Using Simulated Annealing for General Routing Problems with Nodes, Edges, and Arcs….Pages 136-149
Probabilistic Beam Search for the Longest Common Subsequence Problem….Pages 150-161
A Bidirectional Greedy Heuristic for the Subspace Selection Problem….Pages 162-176
EasySyn++: A Tool for Automatic Synthesis of Stochastic Local Search Algorithms….Pages 177-181
Human-Guided Enhancement of a Stochastic Local Search: Visualization and Adjustment of 3D Pheromone….Pages 182-186
Solving a Bi-objective Vehicle Routing Problem by Pareto-Ant Colony Optimization….Pages 187-191
A Set Covering Approach for the Pickup and Delivery Problem with General Constraints on Each Route….Pages 192-196
A Study of Neighborhood Structures for the Multiple Depot Vehicle Scheduling Problem….Pages 197-201
Local Search in Complex Scheduling Problems….Pages 202-206
A Multi-sphere Scheme for 2D and 3D Packing Problems….Pages 207-211
Formulation Space Search for Circle Packing Problems….Pages 212-216
Simple Metaheuristics Using the Simplex Algorithm for Non-linear Programming….Pages 217-221
Back Matter….Pages –