Finding a maximum clique in an arbitrary graph
SIAM Journal on Computing
Tabu Search
Computer Vision
Annealed replication: a new heuristic for the maximum clique problem
Discrete Applied Mathematics
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
A fast algorithm for the maximum clique problem
Discrete Applied Mathematics - Sixth Twente Workshop on Graphs and Combinatorial Optimization
Variable neighborhood search for the maximum clique
Discrete Applied Mathematics - The fourth international colloquium on graphs and optimisation (GO-IV)
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
Evolutionary Computation
A hybrid heuristic for the maximum clique problem
Journal of Heuristics
A study of ACO capabilities for solving the maximum clique problem
Journal of Heuristics
Approximating the maximum vertex/edge weighted clique using local search
Journal of Heuristics
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization
Dynamic local search for the maximum clique problem
Journal of Artificial Intelligence Research
An effective local search for the maximum clique problem
Information Processing Letters
An exact bit-parallel algorithm for the maximum clique problem
Computers and Operations Research
An evolutionary algorithm with guided mutation for the maximum clique problem
IEEE Transactions on Evolutionary Computation
A Multilevel Memetic Approach for Improving Graph k-Partitions
IEEE Transactions on Evolutionary Computation
A study of breakout local search for the minimum sum coloring problem
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
A study of adaptive perturbation strategy for iterated local search
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Breakout local search for the vertex separator problem
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The maximum clique problem (MCP) is one of the most popular combinatorial optimization problems with various practical applications. An important generalization of MCP is the maximum weight clique problem (MWCP) where a positive weight is associate to each vertex. In this paper, we present Breakout Local Search (BLS) which can be applied to both MC and MWC problems without any particular adaptation. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. Extensive experimental evaluations using the DIMACS and BOSHLIB benchmarks show that the proposed approach competes favourably with the current state-of-art heuristic methods for MCP. Moreover, it is able to provide some new improved results for a number of MWCP instances. This paper also reports for the first time a detailed landscape analysis, which has been missing in the literature. This analysis not only explains the difficulty of several benchmark instances, but also justifies to some extent the behaviour of the proposed approach and the used parameter settings.