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A Comparison of Particle Swarm Optimization Algorithms Based on Run-Length Distributions

  • Marco A. Montes de Oca
  • Thomas Stützle
  • Mauro Birattari
  • Marco Dorigo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)

Abstract

In this paper we report an empirical comparison of some of the most influential Particle Swarm Optimization (PSO) algorithms based on run-length distributions (RLDs). The advantage of our approach over the usual report pattern (average iterations to reach a predefined goal, success rates, and standard deviations) found in the current PSO literature is that it is possible to evaluate the performance of an algorithm on different application scenarios at the same time. The RLDs reported in this paper show some of the strengths and weaknesses of the studied algorithms and suggest ways of improving their performance.

Keywords

Particle Swarm Optimization Particle Swarm Solution Quality Particle Swarm Optimization Algorithm Inertia Weight 
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

  • Marco A. Montes de Oca
    • 1
  • Thomas Stützle
    • 1
  • Mauro Birattari
    • 1
  • Marco Dorigo
    • 1
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium

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