|On Generating Fuzzy Systems based on Pareto Multi-objective Cooperative Coevolutionary Algorithm
Zong-Yi Xing, Yong Zhang, Yuan-Long Hou, and Li-Min Jia
International Journal of Control, Automation, and Systems, vol. 5, no. 4, pp.444-455, 2007
Abstract : An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-II algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.
Keyword : Coevolutionary algorithm, fuzzy modeling, fuzzy system, multi-objectives.