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Studying the Performance of Unified Particle Swarm Optimization on the Single Machine Total Weighted Tardiness Problem

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AI 2006: Advances in Artificial Intelligence (AI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4304))

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Abstract

Swarm Intelligence algorithms have proved to be very effective in solving problems on many aspects of Artificial Intelligence. This paper constitutes a first study of the recently proposed Unified Particle Swarm Optimization algorithm on scheduling problems. More specifically, the Single Machine Total Weighted Tardiness problem is considered, and tackled through a scheme that combines Unified Particle Swarm Optimization and the Smallest Position Value technique for the derivation of job sequences from real–valued particles. Experiments on well–known benchmark problems are conducted with promising results, which are reported and discussed.

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Author information

Authors and Affiliations

  1. Computational Intelligence Laboratory (CI Lab), Department of Mathematics, University of Patras, GR, 26110, Patras, Greece

    K. E. Parsopoulos & M. N. Vrahatis

  2. University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, GR, 26110, Patras, Greece

    K. E. Parsopoulos & M. N. Vrahatis

Authors
  1. K. E. Parsopoulos
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  2. M. N. Vrahatis
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Editor information

Editors and Affiliations

  1. DisPRR, National ICT Australia Ltd, QLD, Australia

    Abdul Sattar

  2. School of Computing, University of Tasmania, Sandy Bay, 7005, Tasmania, Australia

    Byeong-ho Kang

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© 2006 Springer-Verlag Berlin Heidelberg

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Parsopoulos, K.E., Vrahatis, M.N. (2006). Studying the Performance of Unified Particle Swarm Optimization on the Single Machine Total Weighted Tardiness Problem. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_80

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  • DOI: https://doi.org/10.1007/11941439_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49787-5

  • Online ISBN: 978-3-540-49788-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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