Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
The ant colony optimization meta-heuristic
New ideas in optimization
Fitness landscapes and memetic algorithm design
New ideas in optimization
Future Generation Computer Systems
Random Constraint Satisfaction: Theory Meets Practice
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A study of ACO capabilities for solving the maximum clique problem
Journal of Heuristics
Searching for maximum cliques with ant colony optimization
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
On MAX - MIN ant system's parameters
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Hybrid metaheuristic algorithms for minimum weight dominating set
Applied Soft Computing
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When solving a combinatorial optimization problem with the Ant Colony Optimization (ACO) metaheuristic, one usually has to find a compromise between guiding or diversifying the search. Indeed, ACO uses pheromone to attract ants. When increasing the sensibility of ants to pheromone, they converge quicker towards a solution but, as a counterpart, they usually find worse solutions. In this paper, we first study the influence of ACO parameters on the exploratory ability of ants. We then study the evolution of the impact of pheromone during the solution process with respect to its cost's management. We finally propose to introduce a preprocessing step that actually favors a larger exploration of the search space at the beginning of the search at low cost. We illustrate our approach on Ant-Solver, an ACO algorithm that has been designed to solve Constraint Satisfaction Problems, and we show on random binary problems that it allows to find better solutions more than twice quicker.