Minimizing binary functions with simulated annealing algorithm with applications to binary tomography

Xuesong Li, Lin Ma

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


The minimization of binary functions finds many applications in practice, and can be solved by the simulated annealing (SA) algorithm. However, the SA algorithm is designed for general combinatorial problems, not specifically for binary problems. Consequently, a direct application of the SA algorithm might not provide optimal performance and efficiency. Therefore, this study specifically investigated the performance of various implementations of the SA algorithm when applied to binary functions. Results obtained in this investigation demonstrated that 1) the SA algorithm can reliably minimize difficult binary functions, 2) a simple technique, analogous to the local search technique used in minimizing continuous functions, can exploit the special structure of binary problems and significantly improve the solution with negligible computational cost, and 3) this technique effectively reduces computational cost while maintaining reconstruction fidelity in binary tomography problems. This study also developed two classes of binary functions to represent the typical challenges encountered in minimization.

Original languageEnglish (US)
Pages (from-to)309-315
Number of pages7
JournalComputer Physics Communications
Issue number2
StatePublished - Feb 2012

Bibliographical note

Copyright 2011 Elsevier B.V., All rights reserved.


  • Binary function
  • Discrete tomography
  • Simulated annealing


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