Chemical Reaction Optimization for the Fuzzy Rule learning problem

In this paper, we utilize Chemical Reaction Optimization (CRO), a newly proposed metaheuristic for global optimization, to design Fuzzy Rule-Based Systems (FRBSs). CRO imitates the interactions of molecules in a chemical reaction. The molecular structure corresponds to a solution, and the potential energy is analogous to the objective function value. Molecules are driven toward the lowest energy stable state, which corresponds to the global optimum of the problem. In the realm of modeling with fuzzy rule-based systems, automatic derivation of fuzzy rules from numerical data plays a critical role. We propose to use CRO with Cooperative Rules (COR) to solve the fuzzy rule learning problem in FRBS. We formulate the learning process of FRBS in the form of a combinatorial optimization problem. Our proposed method COR-CRO is evaluated by two fuzzy modeling benchmarks and compared with other learning algorithms. Simulation results demonstrate that COR-CRO is highly competitive and outperforms many other existing optimization methods.