Comparison between multiobjective GA and PSO for parameter optimization of AT2-FLC for a real application in FPGA
Yazmín Maldonado, Oscar Castillo · 2012
This paper describes the design of a type-2 average fuzzy system on FPGAs and its optimization using multiobjective Particle Swarm Optimization (PSO) and a multiobjective Genetic Algorithm (GA) for the regulation of speed of a DC motor. Based on the concept of evolution, the PSO algorithm and GA are applied to membership functions parameter optimization of type-2 average fuzzy inference systems. Implementations and simulations are carried out in FPGA using the Xilinx system generator. The optimization method was coded in Matlab. The results of comparison PSO with GA were analyzed statistically.