A class of convergent generalized hill climbing algorithms

Alan W. Johnson, Sheldon H. Jacobson

Research output: Contribution to journalArticle

Abstract

Generalized hill climbing (GHC) algorithms have been presented as a modeling framework for local search strategies applied to address intractable discrete optimization (minimization) problems. GHC algorithms include simulated annealing (SA), pure local search (LS), and threshold accepting (TA), among others, as special cases. A particular class of GHC algorithms is designed for discrete optimization problems where the objective function value of a globally optimal solution is known (in this case, the task is to identify an associated optimal solution). This class of GHC algorithms is shown to converge, and six examples are provided that illustrate the diversity of GHC algorithms within this class of convergent algorithms. Implications of these results are discussed.

Original languageEnglish (US)
Pages (from-to)359-373
Number of pages15
JournalApplied Mathematics and Computation
Volume125
Issue number2-3
DOIs
StatePublished - Jan 25 2002

Keywords

  • Convergence
  • Discrete optimization
  • Hill climbing algorithms
  • Local search
  • Simulated annealing

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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