Ant algorithms for discrete optimization
Artificial Life
Future Generation Computer Systems
Genetic, Iterated and Multistart Local Search for the Maximum Clique Problem
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Boosting ACO with a Preprocessing Step
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ants can solve constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
An effective local search for the maximum clique problem
Information Processing Letters
A study of ACO capabilities for solving the maximum clique problem
Journal of Heuristics
Application partitioning on programmable platforms using the ant colony optimization
Journal of Embedded Computing - Embeded Processors and Systems: Architectural Issues and Solutions for Emerging Applications
An Improvement to Ant Colony Optimization Heuristic
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
VERY STRONGLY CONSTRAINED PROBLEMS: AN ANT COLONY OPTIMIZATION APPROACH
Cybernetics and Systems
WSEAS Transactions on Information Science and Applications
An effective local search for the maximum clique problem
Information Processing Letters
Higher order pheromone models in ant colony optimisation
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
An ACO-RFD hybrid method to solve NP-complete problems
Frontiers of Computer Science: Selected Publications from Chinese Universities
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In this paper, we investigate the capabilities of Ant Colony Optimization (ACO) for solving the maximum clique problem. We describe Ant-Clique, an algorithm that successively generates maximal cliques through the repeated addition of vertices into partial cliques. ACO is used to choose, at each step, the vertex to add. We illustrate the behaviour of this algorithm on two representative benchmark instances and we study the impact of pheromone on the solution process. We also experimentally compare Ant-Clique with GLS, a Genetic Local Search approach, and we show that Ant-Clique finds larger cliques, on average, on a majority of DIMACS benchmark instances, even though it does not reach the best known results on some instances.