Journal of Computational Physics
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
How to find the best approximation results
ACM SIGACT News
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
CSL '92 Selected Papers from the Workshop on Computer Science Logic
Simulated annealing with advanced adaptive neighborhood
Second international workshop on Intelligent systems design and application
Second international workshop on Intelligent systems design and application
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
UnitWalk: A New SAT Solver that Uses Local Search Guided by Unit Clause Elimination
Annals of Mathematics and Artificial Intelligence
Hard and easy distributions of SAT problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Analytically Tuned Simulated Annealing Applied to the Protein Folding Problem
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
MultiQuenching Annealing Algorithm for Protein Folding Problem
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Analytically tuned parameters of simulated annealing for the timetabling problem
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Graph k-colorability through threshold accepting and Davis-Putnam
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
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Since the apparition of Simulated Annealing algorithm (SA) it has shown to be an efficient method to solve combinatorial optimization problems. Due to this, new algorithms based on two looped cycles (temperatures and Markov chain) have emerged, one of them have been called Threshold Accepting (TA). Classical algorithms based on TA usually use the same Markov chain length for each temperature cycle, these methods spend a lot of time at high temperatures where the Markov chain length is supposed to be small. In this paper we propose a method based on the neighborhood structure to get the Markov chain length in a dynamic way for each temperature cycle. We implemented two TA algorithms (classical or TACM and proposed or TADM) for SAT. Experimentation shows that the proposed method is more efficient than the classical one since it obtain the same quality of the final solution with less processing time.