Optimization of ANFIS Using Artificial Bee Colony Algorithm for Classification of Malaysian SMEs

Abstract

Adaptive Neuro-Fuzzy Inference System (ANFIS) has been widely applied in industry as well as scientific problems. This is due to its ability to approximate every plant with proper number of rules. However, surge in auto-generated rules, as the inputs increase, adds up to complexity and computational cost of the network. Therefore, optimization is required by pruning the weak rules while, at the same time, achieving maximum accuracy. Moreover, it is important to note that over-reducing rules may result in loss of accuracy. Artificial Bee Colony (ABC) is widely applied swarm-based technique for searching optimum solutions as it uses few setting parameters. This research explores the applicability of ABC algorithm to ANFIS optimization. For the practical implementation, classification of Malaysian SMEs is performed. For validation, the performance of ABC is compared with one of the popular optimization techniques Particle Swarm Optimization (PSO) and recently developed Mine Blast Algorithm (MBA). The evaluation metrics include number of rules in the optimized rule-base, accuracy, and number of iterations to converge. Results indicate that ABC needs improvement in exploration strategy in order to avoid trap in local minima. However, the application of any efficient metaheuristic with the modified two-pass ANFIS learning algorithm will provide researchers with an approach to effectively optimize ANFIS when the number of inputs increase significantly.