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Comparison between genetic algorithms and particle swarm optimization

  • Russell C. Eberhart
  • Yuhui Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1447)

Abstract

This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from one paradigm into the other.

Keywords

Genetic Algorithm Particle Swarm Optimization Problem Space Inertia Weight Elitist Strategy 
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 1998

Authors and Affiliations

  • Russell C. Eberhart
    • 1
  • Yuhui Shi
    • 1
  1. 1.Department of Electrical EngineeringIndiana University Purdue University IndianapolisIndianapolis

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