Evolving Fuzzy Classification System by a Quantum Particle Swarm Optimization Algorithm

This paper discusses how to construct a fuzzy classification system (FCS) effectively and accurately. In the FCS, the initial fuzzy rules are optimized with a quantum bit which has many unique advantages such as small population size, fast convergence, short training time and strong global search ability. After then, in order to accomplish the optimization for the fuzzy space partition and the number of fuzzy rules, this paper propose a method-quantum particle swarm optimization (QPSO) -to improve the initial FCS. The experiment result demonstrates that this method is more efficient than other methods without QPSO.