Modification of the Particle Swarm Optimizer for Locating All the Global Minima

  • K. E. Parsopoulos
  • M. N. Vrahatis


In many optimization applications, escaping from the local minima as well as computing all the global minima of an objective function is of vital importance. In this paper the Particle Swarm Optimization method is modified in order to locate and evaluate all the global minima of an objective function. The new approach separates the swarm properly when a candidate minimizer is detected. This technique can also be used for escaping from the local minima which is very important in neural network training.


Particle Swarm Optimization Local Search Global Minimizer Inertia Weight Neural Network Training 
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 Wien 2001

Authors and Affiliations

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

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