Future Generation Computer Systems
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Ant Colony Optimization
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
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
| Hi-index | 0.00 |
Consultant-Guided Search (CGS) is a recent metaheuristic for combinatorial optimization problems, which has been successfully applied to the Traveling Salesman Problem (TSP). In experiments without local search, it has been able to outperform some of the best Ant Colony Optimization (ACO) algorithms. However, local search is an important part of any ACO algorithm and a comparison without local search can be misleading. In this paper, we investigate if CGS is still able to compete with ACO when all algorithms are combined with local search. In addition, we propose a new variant of CGS for the TSP, which introduces the concept of confidence in relation to the recommendations made by consultants. Our experimental results show that the solution quality obtained by this new CGS algorithm is comparable with or better than that obtained by Ant Colony System and MAX-MIN Ant System with 3-opt local search.