On selecting a satisfying truth assignment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Improving repair-based constraint satisfaction methods by value propagation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Towards a characterisation of the behaviour of stochastic local search algorithms for SAT
Artificial Intelligence
Some optimal inapproximability results
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Improved Approximation Algorithms for the Vertex Cover Problem in Graphs and Hypergraphs
SIAM Journal on Computing
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Evolutionary Algorithms for Vertex Cover
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Simple and Fast: Improving a Branch-And-Bound Algorithm for Maximum Clique
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A fast algorithm for the maximum clique problem
Discrete Applied Mathematics - Sixth Twente Workshop on Graphs and Combinatorial Optimization
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Approximating Maximum Clique by Removing Subgraphs
SIAM Journal on Discrete Mathematics
Linear degree extractors and the inapproximability of max clique and chromatic number
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Many hard examples in exact phase transitions
Theoretical Computer Science
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
Random constraint satisfaction: Easy generation of hard (satisfiable) instances
Artificial Intelligence
A Stochastic Local Search Approach to Vertex Cover
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Simple ingredients leading to very efficient heuristics for the maximum clique problem
Journal of Heuristics
An efficient branch-and-bound algorithm for finding a maximum clique with computational experiments
Journal of Global Optimization
Additive versus multiplicative clause weighting for SAT
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Exact phase transitions in random constraint satisfaction problems
Journal of Artificial Intelligence Research
Optimal schedules for parallelizing anytime algorithms: the case of shared resources
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Dynamic local search for the maximum clique problem
Journal of Artificial Intelligence Research
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
The exponentiated subgradient algorithm for heuristic Boolean programming
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A simple model to generate hard satisfiable instances
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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
Iterated k-opt local search for the maximum clique problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Fast local search for the maximum independent set problem
WEA'08 Proceedings of the 7th international conference on Experimental algorithms
The breakout method for escaping from local minima
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Combining Graph Structure Exploitation and Propositional Reasoning for the Maximum Clique Problem
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Experimental Methods for the Analysis of Optimization Algorithms
Experimental Methods for the Analysis of Optimization Algorithms
Cooperating local search for the maximum clique problem
Journal of Heuristics
Local search with edge weighting and configuration checking heuristics for minimum vertex cover
Artificial Intelligence
Local Search with Configuration Checking for SAT
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
A better approximation ratio for the vertex cover problem
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
An exact algorithm for the maximum clique problem
Operations Research Letters
Local search for Boolean Satisfiability with configuration checking and subscore
Artificial Intelligence
Hi-index | 0.00 |
The Minimum Vertex Cover (MVC) problem is a prominent NP-hard combinatorial optimization problem of great importance in both theory and application. Local search has proved successful for this problem. However, there are two main drawbacks in state-of-the-art MVC local search algorithms. First, they select a pair of vertices to exchange simultaneously, which is timeconsuming. Secondly, although using edge weighting techniques to diversify the search, these algorithms lack mechanisms for decreasing the weights. To address these issues, we propose two new strategies: two-stage exchange and edge weighting with forgetting. The two-stage exchange strategy selects two vertices to exchange separately and performs the exchange in two stages. The strategy of edge weighting with forgetting not only increases weights of uncovered edges, but also decreases some weights for each edge periodically. These two strategies are used in designing a new MVC local search algorithm, which is referred to as NuMVC. We conduct extensive experimental studies on the standard benchmarks, namely DIMACS and BHOSLIB. The experiment comparing NuMVC with state-of-the-art heuristic algorithms show that NuMVC is at least competitive with the nearest competitor namely PLS on the DIMACS benchmark, and clearly dominates all competitors on the BHOSLIB benchmark. Also, experimental results indicate that NuMVC finds an optimal solution much faster than the current best exact algorithm for Maximum Clique on random instances as well as some structured ones. Moreover, we study the effectiveness of the two strategies and the run-time behaviour through experimental analysis.