Diversify Intensification Phases in Local Search for SAT with a New Probability Distribution
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A key challenge in developing efficient local search solvers is to intelligently balance diversification and intensification. This study pro- poses a heuristic that integrates a new dynamic scoring function and two different diversification criteria: variable weights and stagnation weights. Our new dynamic scoring function is formulated to enhance the diversifi- cation capability in intensification phases using a user-defined diversifica- tion parameter. The formulation of the new scoring function is based on a probability distribution to adjust the selecting priorities of the selection between greediness on scores and diversification on variable properties. The probability distribution of variables on greediness is constructed to guarantee the synchronization between the probability distribution func- tions and score values. Additionally, the new dynamic scoring function is integrated with the two diversification criteria. The experiments show that the new heuristic is efficient on verification benchmark, crafted and random instances.
Lecture Notes in Computer Science Vol 8272
© 2013 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
Artificial Intelligence and Image Processing not elsewhere classified