Efficient local search for very large-scale satisfiability problems
ACM SIGART Bulletin
An interior point algorithm to solve computationally difficult set covering problems
Mathematical Programming: Series A and B - Special issue on interior point methods for linear programming: theory and practice
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Improvements to propositional satisfiability search algorithms
Improvements to propositional satisfiability search algorithms
Improved solutions to the Steiner triple covering problem
Information Processing Letters
Fortran subroutines for computing approximate solutions of weighted MAX-SAT problems using GRASP
Discrete Applied Mathematics
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Parallel GRASP for MAX-SAT Problems
PARA '96 Proceedings of the Third International Workshop on Applied Parallel Computing, Industrial Computation and Optimization
Local search algorithms for partial MAXSAT
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Variable-selection heuristics in local search for SAT
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Solving hard set covering problems
Operations Research Letters
Efficient initial solution to extremal optimization algorithm for weighted MAXSAT problem
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
An effective heuristic algorithm for the maximum satisfiability problem
Applied Intelligence
A GRASP algorithm to solve the unicost set covering problem
Computers and Operations Research
High Performing Algorithms for MAP and Conditional Inference in Markov Logic
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
M-GRASP: a GRASP with memory for latency-aware partitioning methods in DVE systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Iterated robust tabu search for MAX-SAT
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Scaling and probabilistic smoothing: dynamic local search for unweighted MAX-SAT
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Boosting learning and inference in Markov logic through metaheuristics
Applied Intelligence
Adaptive memory-based local search for MAX-SAT
Applied Soft Computing
Solving weighted MAX-SAT via global equilibrium search
Operations Research Letters
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For problems SAT and MAX SAT, local search algorithms are widely acknowledged as one of the most effective approaches. Most of the local search algorithms are based on the 1-flip neighborhood, which is the set of solutions obtainable by flipping the truth assignment of one variable. In this paper, we consider r-flip neighborhoods for r = 2, 3, and examine their effectiveness by computational experiments. In the accompanying paper, we proposed new implementations of these neighborhoods, and showed that the expected size of 2-flip neighborhood is O(n + m) and that of 3-flip neighborhood is O(m + t2n), compared to their original size O(n2) andO(n3), respectively, where n is the number of variables, m is the number of clauses and t is the maximum number of appearances of one variable. These are used in this paper under the framework of tabu search and other metaheuristic methods, and compared with other existing algorithms with 1-flip neighborhood. The results exhibit good prospects of larger neighborhoods.