Potential of Heterogeneity in Collective Behaviors: A Case Study on Heterogeneous Swarms

  • Daniela Kengyel
  • Heiko Hamann
  • Payam Zahadat
  • Gerald Radspieler
  • Franz Wotawa
  • Thomas Schmickl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9387)


Research in swarm robotics and collective behaviors is often focused on homogeneous swarms. However, heterogeneity in behaviors can be advantageous as we know, for example, from studies on social insects. Our objective is to study the hypothesis that there are potential advantages of heterogeneous swarms over homogeneous swarms in an aggregation scenario inspired by behaviors of juvenile honeybees. Even without task switching – that is, with predefined, static roles for certain swarm fractions – we find in our case study that heterogeneous swarms can outperform homogeneous swarms for a predetermined set of basic behaviors. We use methods of evolutionary computation to define behaviors imitating those found in honeybees (random walkers, wall followers, goal finders, immobile agents) and also to find well-adapted swarm fractions of different predetermined behaviors. Our results show that non-trivial distributions of behaviors give better aggregation performance.


Random Walker Multiagent System Behavior Type Collective Behavior Swarm Intelligence 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Daniela Kengyel
    • 1
  • Heiko Hamann
    • 2
  • Payam Zahadat
    • 1
  • Gerald Radspieler
    • 1
  • Franz Wotawa
    • 3
  • Thomas Schmickl
    • 1
  1. 1.Artificial Life Laboratory at the Department of ZoologyKarl-Franzens University GrazGrazAustria
  2. 2.Department of Computer ScienceUniversity of PaderbornPaderbornGermany
  3. 3.Institute for Software TechnologyGraz University of TechnologyGrazAustria

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