Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Integer and combinatorial optimization
Integer and combinatorial optimization
Future Generation Computer Systems
Tabu Search
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Ant Colony Optimization
Constraint-Based Local Search
A study of ACO capabilities for solving the maximum clique problem
Journal of Heuristics
Ant Colony Optimization for Multi-Objective Optimization Problems
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Integration of ACO in a Constraint Programming Language
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Ants can solve constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Expert Systems with Applications: An International Journal
Boosting search based testing by using constraint based testing
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
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We introduce an approach which combines ACO (Ant Colony Optimization) and IBM ILOG CP Optimizer for solving COPs (Combinatorial Optimization Problems). The problem is modeled using the CP Optimizer modeling API. Then, it is solved in a generic way by a two-phase algorithm. The first phase aims at creating a hot start for the second: it samples the solution space and applies reinforcement learning techniques as implemented in ACO to create pheromone trails. During the second phase, CP Optimizer performs a complete tree search guided by the pheromone trails previously accumulated. The first experimental results on knapsack, quadratic assignment and maximum independent set problems show that this new algorithm enhances the performance of CP Optimizer alone.