Martínez Ríos, Félix Orlando
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Martínez Ríos, Félix Orlando
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Martínez Ríos, Félix Orlando
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fmartin
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ORCID
Scopus Author ID
24339038500
Researcher ID
N-7502-2018

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Golden Ratio Annealing for Satisfiability Problems Using Dynamically Cooling Schemes

2008 , Frausto-Solís, Juan , Martínez Ríos, Félix Orlando

Abstract Satisfiability (SAT) Problem is an NP-Complete problem which means no deterministic algorithm is able to solve it in a polynomial time. Simulated Annealing (SA) can find very good solutions of SAT instances if its control parameters are correctly tuned. SA can be tuned experimentally or by using a Markov approach; the latter has been shown to be the most efficient one. Moreover Golden Ratio (GR) is an unconventional technique used to solve many problems. In this paper a new algorithm named Golden Ratio for Simulated Annealing (GRSA) is presented; it is tuned for three different cooling schemes. GRSA uses GR to dynamically decrease the SA temperature and a Markov Model to tune its parameters. Two SA tuned versions are compared in this paper: GRSA and a classical SA. Experimentation shows that the former is much more efficient than the latter. © Springer Nature

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A hybrid simulated annealing and threshold accepting for satisfiability problems using dynamically cooling schemes

2007 , Martínez Ríos, Félix Orlando , Frausto-Solís, Juan

For Satisfiability (SAT) Problem there is not a deterministic algorithm able to solve it in a polynomial time. Simulated Annealing (SA) and similar algorithms like Threshold Accepting (TA) are able to find very good solutions of SAT instances only if their control parameters are correctly tuned. Classical TA usually uses the same Markov chain length for each temperature cycle but they spend a lot of time. In this paper a method based on the neighborhood structure to get the Markov chain length in a dynamical way for each temperature cycle is proposed. Three cooling schemes are also presented in the paper. The experimentation presented in the paper shows that the proposed method is more efficient than the classical one.

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Simulated Annealing for SAT Problems Using Dynamic Markov Chains with Linear Regression Equilibrium

2008 , Martínez Ríos, Félix Orlando , Frausto-Solís, Juan

Since the appearance of Simulated Annealing (SA) algorithm it has shown to be an efficient method to solve combinatorial optimization problems. This algorithm is based on two cycles: the external or temperature cycle and the internal or Metropolis Cycle. In this paper a new SA method named LRSA is presented. LRSA dynamically finds the equilibrium in the Metropolis cycle by using Linear Regression. Experimentation shows that the proposed method is more efficient than the classical one, since it obtains the same quality in the final solution with less processing time.

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