Natural Computing

, Volume 1, Issue 2, pp 235–306

Recent approaches to global optimization problems through Particle Swarm Optimization

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

DOI: 10.1023/A:1016568309421

Cite this article as:
Parsopoulos, K. & Vrahatis, M. Natural Computing (2002) 1: 235. doi:10.1023/A:1016568309421

Abstract

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and ℓ1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.

Differential EvolutionEvolutionary ComputationGlobal OptimizationInteger ProgrammingMatlab Code ImplementationMinimax ProblemsMultiobjective OptimizationNoisy ProblemsParticle Swarm OptimizationSwarm Intelligence

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

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