TY - JOUR

T1 - Minimizing binary functions with simulated annealing algorithm with applications to binary tomography

AU - Li, Xuesong

AU - Ma, Lin

PY - 2012/2/1

Y1 - 2012/2/1

N2 - 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.

AB - 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.

KW - Binary function

KW - Discrete tomography

KW - Simulated annealing

UR - http://www.scopus.com/inward/record.url?scp=81455131439&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=81455131439&partnerID=8YFLogxK

U2 - 10.1016/j.cpc.2011.10.011

DO - 10.1016/j.cpc.2011.10.011

M3 - Article

AN - SCOPUS:81455131439

VL - 183

SP - 309

EP - 315

JO - Computer Physics Communications

JF - Computer Physics Communications

SN - 0010-4655

IS - 2

ER -