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

  • K. E. Parsopoulos
  • M. N. Vrahatis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)

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.

Keywords

Particle Swarm Optimization Algorithm Swarm Intelligence Variable Neighborhood Search Swarm Intelligence Algorithm Exploitation Ability 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • K. E. Parsopoulos
    • 1
    • 2
  • M. N. Vrahatis
    • 1
    • 2
  1. 1.Computational Intelligence Laboratory (CI Lab), Department of MathematicsUniversity of PatrasPatrasGreece
  2. 2.University of Patras Artificial Intelligence Research Center (UPAIRC)University of PatrasPatrasGreece

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