An Analysis of Local Search for the Bi-objective Bidimensional Knapsack Problem

  • Leonardo C. T. Bezerra
  • Manuel López-Ibáñez
  • Thomas Stützle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7832)

Abstract

Local search techniques are increasingly often used in multi-objective combinatorial optimization due to their ability to improve the performance of metaheuristics. The efficiency of multi-objective local search techniques heavily depends on factors such as (i) neighborhood operators, (ii) pivoting rules and (iii) bias towards good regions of the objective space. In this work, we conduct an extensive experimental campaign to analyze such factors in a Pareto local search (PLS) algorithm for the bi-objective bidimensional knapsack problem (bBKP). In the first set of experiments, we investigate PLS as a stand-alone algorithm, starting from random and greedy solutions. In the second set, we analyze PLS as a post-optimization procedure.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Leonardo C. T. Bezerra
    • 1
  • Manuel López-Ibáñez
    • 1
  • Thomas Stützle
    • 1
  1. 1.IRIDIAUniversité Libre de BruxellesBrusselsBelgium

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