Journal of Global Optimization

, Volume 30, Issue 2–3, pp 301–318

On the Convergence of a Population-Based Global Optimization Algorithm

  • Ş. İlker Birbil
  • Shu-Cherng Fang
  • Ruey-Lin Sheu

DOI: 10.1007/s10898-004-8270-3

Cite this article as:
Birbil, Ş.İ., Fang, SC. & Sheu, RL. J Glob Optim (2004) 30: 301. doi:10.1007/s10898-004-8270-3


In global optimization, a typical population-based stochastic search method works on a set of sample points from the feasible region. In this paper, we study a recently proposed method of this sort. The method utilizes an attraction-repulsion mechanism to move sample points toward optimality and is thus referred to as electromagnetism-like method (EM). The computational results showed that EM is robust in practice, so we further investigate the theoretical structure. After reviewing the original method, we present some necessary modifications for the convergence proof. We show that in the limit, the modified method converges to the vicinity of global optimum with probability one.

stochastic search method population-based algorithm convergence with probability one 

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Ş. İlker Birbil
    • 1
  • Shu-Cherng Fang
    • 2
  • Ruey-Lin Sheu
    • 3
  1. 1.Erasmus Research Institute of Management (ERIM)Erasmus UniversityThe Netherlands
  2. 2.Industrial Engineering and Operations ResearchNorth Carolina State UniversityNCU.S.A.
  3. 3.Department of MathematicsNational Cheng-Kung UniversityTainanTaiwan, R.O.C.

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