Earticle

초록

영어

Particle swarm optimization (PSO) is a population-based stochastic optimization originat- ing from artificial life and evolutionary computation. PSO is motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. Its properties of low constraint on the continuity of objective function and ability of adapting to the dynamic environment make PSO become one of the most important swarm intelligence algorithms. However, compared to the various version of modified PSO and the corresponding applica- tions in many domains, there has been very little research on the PSO’s convergence analysis. So the current paper, to begin with, elaborates the basic principles of standard PSO. Then the existing work on the convergence analyses of PSO in the literatures is thoroughly surveyed, which plays an important role in establishing the solid theoretical foundation for PSO algorithm. In the end, some important conclusions and possible research directions of PSO that need to be studied in the future are proposed.

목차

Abstract
 1. Introduction
 2. Standard PSO
 3. Convergence Analyses of PSO
  3.1. Convergence Analysis with Constriction Coefficient
  3.2. Convergence Analysis with Limit
  3.3. Convergence Analysis with Differential Equation
  3.4. Convergence Analysis with Matrix
  3.5. Convergence Analysis with Difference Equation
  3.6. Convergence Analysis with Z Transformation
  3.7. Other Convergence Analyses
 4. Conclusion and Expectation
 Acknowledgements
 References

키워드

저자

• Dong ping Tian [ Institute of Computer Software, Baoji University of Arts and Sciences, Institute of Computational Information Science, Baoji University of Arts and Sciences, Baoji, Shaanxi, 721007, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605