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 -