Designing fuzzy rule base using Spider Monkey Optimization Algorithm in cooperative framework

Abstract

The paper focusses on the implementation of cooperative Spider Monkey Optimization Algorithm (SMO) to design and optimize the fuzzy rule base. Spider Monkey Optimization Algorithm is a fission-fusion based Swarm Intelligence algorithm. Cooperative Spider Monkey Algorithm is an off-line algorithm used to optimize all the free parameters in a fuzzy rule base. The Spider Monkeys are divided into various groups the solution from each group represents a fuzzy rule. These groups work in a cooperative way to design the whole fuzzy rule base. Simulation on fuzzy rules of two nonlinear controllers is done with a parametric study to verify the performance of the algorithm. It is observed that the root mean square error (RMSE) is least in the case of SMO than the other evolutionary algorithms applied in the literature to solve the problem of fuzzy rule designs like Particle Swarm Optimization (PSO), Ant Colony Optimization algorithm (ACO) algorithms.