Comparison between ant colony and genetic algorithms for fuzzy system optimization

Abstract

In this paper we show some of the results that we obtain with different evolutionary methods on a Mamdani Fuzzy Inference System (FIS); we work with Hierarchical Genetic Algorithms (HGA) and the Ant Colony Optimization (ACO), the fuzzy inference system controls a benchmark problem which is "The Ball and Beam" system, optimizing the fuzzy rules of the system. Firs, we work to optimize the FIS that is structured by two inputs (the error and the derived error), an output (the angle of the beam so that we can get the ball position on it); and the 44 fuzzy rules that we used to be reduced with the evolutionary methods (HGA, ACO), so that we could make the comparisons between them via average and standard deviation, and concluding with the best evolutionary method for a fuzzy system optimization control problem. © 2008 Springer-Verlag Berlin Heidelberg.