Fuzzy rule weights optimization based on Imperialist Competitive Algorithm

Abstract

Fuzzy rule-based systems are appropriate tools to deal with the classification problems due to their interpretabilities and accuracies. The aim of the paper is to improve the performance of Fuzzy Rule-Based Classification Systems (FRBCS) by learning their weights using Imperialist Competitive Algorithm (ICA). Among the evolutionary algorithms, here, ICA is chosen to solve the premature convergence problem of the other competitive algorithms. To evaluate the proposed method, several datasets belonged to the UCI database are selected as the benchmark and applied to the proposed FRBCS optimized by ICA and finally compared to the other FRBCS which their weights are adjusted by other evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The achieved results on most of the datasets imply on the superiority of the proposed combinational scheme compared to the other similar rivals.