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Memetic Computing

, Volume 6, Issue 1, pp 19–29 | Cite as

A bare-bones ant colony optimization algorithm that performs competitively on the sequential ordering problem

  • Ahmed Ezzat
  • Ashraf M. Abdelbar
  • Donald C. WunschII
Regular research paper

Abstract

EigenAnt is a bare-bones ant colony optimization algorithm that has been proven to converge to the optimal solution under certain conditions. In this paper, we extend EigenAnt to the sequential ordering problem (SOP), comparing its performance to Gambardella et al.’s enhanced ant colony system (EACS), a model that has been found to have state-of-the-art performance on the SOP. Our experimental results, using the SOPLIB2006 instance library, indicate that there is no statistically significant difference in performance between our proposed method and the state-of-the-art EACS method.

Keywords

Ant colony optimization Sequential ordering problem  Swarm intelligence EigenAnt algorithm Ant colony system Metaheuristic Traveling salesman problem 

Notes

Acknowledgments

The partial support of the National Science Foundation, the Missouri University of Science and Technology Center for Infrastructure Engineering Studies and Intelligent Systems Center, and the Mary K. Finley Missouri Endowment are gratefully acknowledged. We would like to thank Jayadeva for providing the Matlab source code for the EigenAnt algorithm. Although our implementation, in C, did not directly incorporate this code, having access to it was useful in validating our implementation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ahmed Ezzat
    • 1
  • Ashraf M. Abdelbar
    • 2
  • Donald C. WunschII
    • 3
  1. 1.Department of Computer Science and EngineeringAmerican University in CairoCairoEgypt
  2. 2.Department of Mathematics and Computer ScienceBrandon UniversityBrandonCanada
  3. 3.Department of Electrical and Computer EngineeringMissouri University of Science and TechnologyRollaUSA

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