A self-generating fuzzy system with ant and particle swarm cooperative optimization

This paper proposes a self-generating fuzzy system with a learning ability from a combination of the on-line self-aligning clustering (OSAC) algorithm and ant and particle swarm cooperative optimization (APSCO). The proposed OSAC algorithm not only helps generate rules from on-line training data, but also helps avoid generating highly overlapping fuzzy sets. Once a new rule is generated, APSCO optimizes the corresponding antecedent and consequent parameters. In APSCO, ant colony and particle swarm coexist in a population, and both search for an optimal parameter solution simultaneously in each iteration. Ant paths not only help determine the consequent parameters of generated rules, they also help generate auxiliary particles. Well-performing particles are selected from the auxiliary particles and original particles. And these selected particles cooperate to find a better solution through particle swarm optimization. This paper applies the proposed self-generating fuzzy system to different fuzzy controller design problems, and compares it with other genetic and swarm intelligence algorithms and their hybrids to verify system performance.