Ant algorithms for discrete optimization
Artificial Life
Constraint Handling in Genetic Algorithms: The Set Partitioning Problem
Journal of Heuristics
An Island Model Based Ant System with Lookahead for the Shortest Supersequence Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An Ant-Based Framework for Very Strongly Constrained Problems
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Solving Dial-a-Ride Problems with a Low-Level Hybridization of Ants and Constraint Programming
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Integration of constraint programming and metaheuristics
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
New ideas for applying ant colony optimization to the set covering problem
Computers and Industrial Engineering
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Concave minimum cost network flow problems solved with a colony of ants
Journal of Heuristics
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Set Covering Problems and Set Partitioning Problems can model several real life situations. In this paper, we solve some benchmarks of them with Ant Colony Optimization algorithms and some hybridizations of Ant Colony Optimization with Constraint Programming techniques. A lookahead mechanism allows the incorporation of information on the anticipated decisions that are beyond the immediate choice horizon. The ants solutions may contain redundant components which can be eliminated by a fine tuning after the solution, then we explore Post Processing procedures too, which consist in the identification and replacement of the columns of the ACO solution in each iteration by more effective columns. Computational results are presented showing the advantages to use additional mechanisms to Ant Colony Optimization.