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Search Space Analysis of the Linear Ordering Problem

  • Tommaso Schiavinotto
  • Thomas Stützle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)

Abstract

The Linear Ordering Problem (LOP) is an NP-hard combinatorial optimization problem that arises in a variety of applications and several algorithmic approaches to its solution have been proposed. However, few details are known about the search space characteristics of LOP instances. In this article we develop a detailed study of the LOP search space. The results indicate that, in general, LOP instances show high fitness-distance correlations and large autocorrelation length but also that there exist significant differences between real-life and randomly generated LOP instances. Because of the limited size of real-world instances, we propose new, randomly generated large real-life like LOP instances which appear to be much harder than other randomly generated instances. Additionally, we propose a rather straightforward Iterated Local Search algorithm, which shows better performance than several state-of-the-art heuristics.

Keywords

Local Search Global Optimum Local Search Algorithm Matrix Entry Scatter Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Tommaso Schiavinotto
    • 1
  • Thomas Stützle
    • 1
  1. 1.Intellectics GroupDarmstadt University of TechnologyDarmstadtGermany

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