Ant Colony Optimization
A study of greedy, local search, and ant colony optimization approaches for car sequencing problems
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Encodings of the SEQUENCE constraint
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Revisiting the sequence constraint
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Ants can solve constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Car sequencing with constraint-based ACO
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Edge detection of laser range image based on a fast adaptive ant colony algorithm
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
| Hi-index | 0.00 |
The Ant Colony Optimization (ACO) meta-heuristic [1] has proven its efficiency to solve hard combinatorial optimization problems. However most works have focused on designing efficient ACO algorithms for solving specific problems, but not on integrating ACO within declarative languages so that solving a new problem with ACO usually implies a lot of procedural programming. Our approach is thus to explore the tight integration of Constraint Programming (CP) with ACO. Our research is based upon ILOG Solver, and we use its modeling language and its propagation engine, but the search is guided by ACO. This approach has the benefit of reusing all the work done at the modeling level as well as the code dedicated to constraint propagation and verification.