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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2008: Simulated Evolution and Learning pp 411–420Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Communication, Computer and Systems Sciences, University of Genova, Via Opera Pia 13, 16145, Genova, Italy

    Davide Anghinolfi, Antonio Boccalatte & Massimo Paolucci

  2. Department of Computer Science and Software Engineering, The University of Melbourne, 111 Barry St, 3053, Carlton, Victoria, Australia

    Christian Vecchiola

Authors
  1. Davide Anghinolfi
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  2. Antonio Boccalatte
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  3. Massimo Paolucci
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  4. Christian Vecchiola
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Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

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© 2008 Springer-Verlag Berlin Heidelberg

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Cite this paper

Anghinolfi, D., Boccalatte, A., Paolucci, M., Vecchiola, C. (2008). Performance Evaluation of an Adaptive Ant Colony Optimization Applied to Single Machine Scheduling. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_42

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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