Hybrid Population-Based Algorithms for the Bi-Objective Quadratic Assignment Problem

  • Manuel López-Ibáñez
  • Luís Paquete
  • Thomas Stützle
Article

Abstract

We present variants of an ant colony optimization (MO-ACO) algorithm and of an evolutionary algorithm (SPEA2) for tackling multi-objective combinatorial optimization problems, hybridized with an iterative improvement algorithm and the robust tabu search algorithm. The performance of the resulting hybrid stochastic local search (SLS) algorithms is experimentally investigated for the bi-objective quadratic assignment problem (bQAP) and compared against repeated applications of the underlying local search algorithms for several scalarizations. The experiments consider structured and unstructured bQAP instances with various degrees of correlation between the flow matrices. We do a systematic experimental analysis of the algorithms using outperformance relations and the attainment functions methodology to asses differences in the performance of the algorithms. The experimental results show the usefulness of the hybrid algorithms if the available computation time is not too limited and identify SPEA2 hybridized with very short tabu search runs as the most promising variant.

Mathematics Subject Classifications (2000)

68W20 68T20 90B80 90C27 90C29 

Key words

multi-objective quadratic assignment problem hybrid algorithms stochastic local search ant colony optimization evolutionary algorithms tabu search 

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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Manuel López-Ibáñez
    • 1
  • Luís Paquete
    • 2
  • Thomas Stützle
    • 3
  1. 1.Centre for Emergent Computing, School of the Built EnvironmentNapier UniversityEdinburghUK
  2. 2.Faculdade de Economia and Centro de Sistemas InteligentesUniversidade do AlgarveFaroPortugal
  3. 3.Université Libre de Bruxelles, IRIDIABrusselsBelgium

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