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Introducing the Environment in Ant Colony Optimization

  • Antonio Mucherino
  • Stefka Fidanova
  • Maria Ganzha
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 655)

Abstract

Meta-heuristics are general-purpose methods for global optimization, which take generally inspiration from natural behaviors and phenomena. Among the others, Ant Colony Optimization (ACO) received particular interest in the last years. In this work, we introduce the environment in ACO, for the meta-heuristic to perform a more realistic simulation of the ants’ behavior. Computational experiments on instances of the GPS Surveying Problem (GSP) show that the introduction of the environment in ACO allows us to improve the quality of obtained solutions.

Keywords

Global Position System Attraction Domain Variable Neighborhood Search Heuristic Information Weighted Directed Graph 
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.

Notes

Acknowledgments

This work was partially supported by two grants of the Bulgarian National Scientific Fund: “Efficient Parallel Algorithms for Large Scale Computational Problems” and “InterCriteria Analysis. A New Approach to Decision Making”.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Antonio Mucherino
    • 1
  • Stefka Fidanova
    • 2
  • Maria Ganzha
    • 3
  1. 1.IRISAUniversity of Rennes 1RennesFrance
  2. 2.BASUniversity of SofiaSofiaBulgaria
  3. 3.SRIPolish Academy of ScienceWarsawPoland

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