Simulated annealing and combinatorial optimization

  • Authors:
  • Surendra Nahar;Sartaj Sahni;Eugene Shragowitz

  • Affiliations:
  • University of Minnesota;University of Minnesota;Control Data Corporation

  • Venue:
  • DAC '86 Proceedings of the 23rd ACM/IEEE Design Automation Conference
  • Year:
  • 1986

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Abstract

We formulate a class of adaptive heuristics for combinatorial optimization. Recently proposed methods such as simulated annealing, probabilistic hill climbing, and sequence heuristics, as well as classical perturbation methods are all members of this class of adaptive heuristics. We expose the issues involved in using an adaptive heuristic in general, and simulated annealing, probabilistic hill climbing, and sequence heuristics in particular. These issues are investigated experimentally.