Fuzzy Controller Design by Hybrid Evolutionary Learning Algorithms

An evolutionary fuzzy system that automates the design of fuzzy systems by hybridizing multi-group genetic algorithm and particle swarm optimization, called F-MGAPSO, is proposed in this paper. By F-MGAPSO, we aim to simultaneously design the number of fuzzy rules and free parameters in a fuzzy system. In initial population, the number of rules encoded in each individual is randomly assigned, and the individuals with equal number of rules constitute the same group. Evolution of population consists of three major operations: group enhancement, variable-length individual crossover and mutation, where group enhancement is to enhance elites in each group by local version of particle swam optimization, respectively. To demonstrate the performance, F-MGAPSO is applied to fuzzy control of a nonlinear plant