A Cooperative System of Metaheuristics

Abstract

Hybrid systems give more flexible mechanisms for solving complex problems that can be very difficult to solve using less tolerant approaches. Therefore, a hybrid system will be the most suitable tool in order to cope with the algorithm-instance problem, which says that it is possible that an algorithm and its parameters that obtain good results for an instance of a problem, do not get the same results for another instance of the same problem. All this leads us to use different algorithms to solve combinatorial optimization problems within a single coordinated schema, that is a hybrid cooperative system of metaheuristics. In order to build this system we have proposed a methodology for the construction of a hybrid system, based on data mining and soft computing. In order to test the usefulness of this methodology two hybrid systems based on a fuzzy model have been constructed to solve the knapsack problem. The first system coordinates two metaheuristics, a genetic algorithm and a tabu search. The second one adds a third metaheuristic, simulated annealing, in order to check the robustness of the system and its capacity of obtaining higher quality solutions when a metaheuristic is added. Results obtained by this systems and a comparison with the ones obtained with individual metaheuristics are shown.