A hybrid descent method for global optimization

Ka Fai Cedric Yiu, Y. Liu, K. L. Teo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

61 Citations (Scopus)

Abstract

In this paper, a hybrid descent method, consisting of a simulated annealing algorithm and a gradient-based method, is proposed. The simulated annealing algorithm is used to locate descent points for previously converged local minima. The combined method has the descent property and the convergence is monotonic. To demonstrate the effectiveness of the proposed hybrid descent method, several multi-dimensional non-convex optimization problems are solved. Numerical examples show that global minimum can be sought via this hybrid descent method.
Original languageEnglish
Pages (from-to)229-238
Number of pages10
JournalJournal of Global Optimization
Volume28
Issue number2
DOIs
Publication statusPublished - 1 Feb 2004

Keywords

  • Descent method
  • Global minimum
  • Simulating annealing

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

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