Nonlinear Fuzzy Modelling of Dynamic Objects with Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm

Abstract

Algorithms based on populations are a very popular family of methods for solving optimization problems. One of the more frequently used representatives of this group is the Particle Swarm Optimization algorithm. The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Such a mechanism exists in the FSHPSO-E algorithm presented in our previous paper. To test it, we used the set of benchmark functions widely adapted in the literature. The results proved effectiveness, efficiency, and scalability of this solution. In this paper, we show the effectiveness of this method in solving practical problems of optimization of fuzzy-neural systems used to model non-linear dynamic objects.