Metaheuristics pp 325-344 | Cite as

Using Experimental Design to Analyze Stochastic Local Search Algorithms for Multiobjective Problems

  • Luís Paquete
  • Thomas Stützle
  • Manuel López-Ibáñez
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 39)

Abstract

Stochastic Local Search (SLS) algorithms can be seen as being composed of several algorithmic components, each playing some specific role with respect to overall performance. This article explores the application of experimental design techniques to analyze the effect of components of SLS algorithms for Multiobjective Combinatorial Optimization problems, in particular for the Biobjective Quadratic Assignment Problem. The analysis shows that there exists a strong dependence between the choices for these components and various instance features, such as the structure of the input data and the correlation between the objectives.

Keywords

Approximation methods and heuristics combinatorial optimization multiple objective and goal programming design of experiments 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Luís Paquete
    • 1
  • Thomas Stützle
    • 2
  • Manuel López-Ibáñez
    • 3
  1. 1.Faculdade de Economia & CSI – Centro de Sistemas InteligentesUniversidade do AlgarveCampus de GambelasPortugal
  2. 2.Iridia, Cp 194/6Université Libre de BruxellesAvenue F. Roosevelt 50Belgium
  3. 3.School of the Built EnvironmentNapier UniversityMerchiston CampusUK

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