Information Theoretic Measures for Ant Colony Optimization

  • Gunnar Völkel
  • Markus Maucher
  • Christoph Müssel
  • Uwe Schöning
  • Hans A. KestlerEmail author
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


We survey existing measures to analyze the search behavior of Ant Colony Optimization (ACO) algorithms and introduce a new uncertainty measure for characterizing three ACO variants. Unlike previous measures, the group uncertainty allows for quantifying the exploration of the search space with respect to the group assignment. We use the group uncertainty to analyze the search behavior of Group-Based Ant Colony Optimization.


Uncertainty Measure Shannon Entropy Combinatorial Optimization Problem Vehicle Route Problem Solution Component 
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The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement n602783 to HAK, the German Research Foundation (DFG, SFB 1074 project Z1 to HAK), and the Federal Ministry of Education and Research (BMBF, Gerontosys II, Forschungskern SyStaR, project ID 0315894A to HAK).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Gunnar Völkel
    • 1
  • Markus Maucher
    • 2
  • Christoph Müssel
    • 2
  • Uwe Schöning
    • 3
  • Hans A. Kestler
    • 4
    Email author
  1. 1.Institute of Theoretical Computer Science and Core Unit Medical Systems BiologyUlm UniversityUlmGermany
  2. 2.Medical Systems BiologyUlm UniversityUlmGermany
  3. 3.Institute of Theoretical Computer ScienceUlm UniversityUlmGermany
  4. 4.Institute of Medical Systems BiologyUniversität UlmUlmGermany

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