Parallel Simulated Annealing by Mixing of States

King Wai Chu, Yuefan Deng, John Reinitz

Research output: Contribution to journalArticlepeer-review


We report the results of testing the performance of a new, efficient, and highly general-purpose parallel optimization method, based upon simulated annealing. This optimization algorithm was applied to analyze the network of interacting genes that control embryonic development and other fundamental biological processes. We found several sets of algorithmic parameters that lead to optimal parallel efficiency for up to 100 processors on distributed-memory MIMD architectures. Our strategy contains two major elements. First, we monitor and pool performance statistics obtained simultaneously on all processors. Second, we mix states at intervals to ensure a Boltzmann distribution of energies. The central scientific issue is the inverse problem, the determination of the parameters of a set of nonlinear ordinary differential equations by minimizing the total error between the model behavior and experimental observations.

Original languageEnglish (US)
Pages (from-to)646-662
Number of pages17
JournalJournal of Computational Physics
Issue number2
StatePublished - Jan 20 1999


  • Inverse problems
  • Parallel processing
  • Simulated annealing

ASJC Scopus subject areas

  • Numerical Analysis
  • Modeling and Simulation
  • Physics and Astronomy (miscellaneous)
  • General Physics and Astronomy
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics


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