Comparison between genetic algorithm and differential evolution tuning a fuzzy controller

Abstract

This work brings a performance comparison between the genetic algorithm (GA) and differential evolution (DE) in tuning the parameters of a Fuzzy controller applied to an armature-controlled DC motor. Both heuristics are consolidated, being GA the most popular in tuning fuzzy parameters. To make the comparison, the results found after 10 iterations are presented, as well as the box plots obtained from the collected data. The methodologies presented satisfactory answers, showing punctual differences between one and the other for the presented problem: the AG depends more on an assertive initial population, while the ED does not, managing to do better local searches.