Group-based Evolutionary Swarm Intelligence for Recurrent Fuzzy Controller Design
Chia‐Feng Juang, I‐Fang Chung, Shin-Kuan Chen · 2006
Recurrent fuzzy controller design by the hybrid of multi-group genetic algorithm and particle swarm optimization (R-MGAPSO), is proposed in this paper. The recurrent fuzzy controller designed here is the Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN). Both the number of fuzzy rules and parameters in TRFN are designed concurrently by R-MGAPSO. Evolution of population consists of three major operations: group enhancement by particle swarm optimization, variable-length individual crossover and mutation. To verify the performance of R-MGAPSO, control of a dynamic plant is simulated and compared with other genetic algorithms.