Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
ACO algorithms for the quadratic assignment problem
New ideas in optimization
Future Generation Computer Systems
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Population Based Approach for ACO
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Toward the Formal Foundation of Ant Programming
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
Consultant-guided search: a new metaheuristic for combinatorial optimization problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A hybrid heuristic approach for solving the generalized traveling salesman problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Path Relinking with Multi-Start Tabu Search for the Quadratic Assignment Problem
International Journal of Swarm Intelligence Research
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Consultant-Guided Search (CGS) is a recent swarm intelligence metaheuristic for combinatorial optimization problems, inspired by the way real people make decisions based on advice received from consultants. Until now, CGS has been successfully applied to the Traveling Salesman Problem. Because a good metaheuristic should be able to tackle efficiently a large variety of problems, it is important to see how CGS behaves when applied to other classes of problems. In this paper, we propose an algorithm for the Quadratic Assignment Problem (QAP), which hybridizes CGS with a local search procedure. Our experimental results show that CGS is able to compete in terms of solution quality with one of the best Ant Colony Optimization algorithms, the MAX-MIN Ant System.