Performance Evaluation of an Adaptive Ant Colony Optimization Applied to Single Machine Scheduling

  • Davide Anghinolfi
  • Antonio Boccalatte
  • Massimo Paolucci
  • Christian Vecchiola
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)


We propose a self-adaptive Ant Colony Optimization (AD-ACO) approach that exploits a parameter adaptation mechanism to reduce the requirement of a preliminary parameter tuning. The proposed AD-ACO is based on an ACO algorithm adopting a pheromone model with a new global pheromone update mechanism. We applied this algorithm to the single machine total weighted tardiness scheduling problem with sequence-dependent setup times and we executed an experimental campaign on a benchmark available in literature. Results, compared with the ones produced by the ACO algorithm without adaptation mechanism and with those obtained by recently proposed metaheuristic algorithms for the same problem, highlight the quality of the proposed approach.


Ant Colony Optimization Metaheuristics Scheduling 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Davide Anghinolfi
    • 1
  • Antonio Boccalatte
    • 1
  • Massimo Paolucci
    • 1
  • Christian Vecchiola
    • 2
  1. 1.Department of Communication, Computer and Systems SciencesUniversity of GenovaGenovaItaly
  2. 2.Department of Computer Science and Software EngineeringThe University of MelbourneCarltonAustralia

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