Improvement to the cooperative rules methodology by using the ant colony system algorithm

Abstract

The cooperative rules (COR) methodology [2] is based on a combinatorial search of cooperative rules performed over a set of previously generated candidate rule consequents. It obtains accurate models preserving the highest interpretability of the linguistic fuzzy rule-based systems.

Once the good behavior of the COR methodology has been proven in previous works, this contribution focuses on developing the process with a novel kind of metaheuristic algorithm: the ant colony system one. Thanks to the capability of this algorithm to include heuristic information, the learning process is accelerated without model accuracy losses.

Its behavior is successful compared with other processes based on genetic algorithms and simulated annealing when solving two modeling applications.