Features of Metaheuristic Algorithm for Integration with ANFIS Model

EasyChair Preprint 7594

Abstract

In recent years, many applications based on the Neural Network, Neuro-Fuzzy, and optimization algorithms have been more common for solving regression and classification problems. In the Adaptive neuro-fuzzy inference system(ANFIS), many researchers used the adaption of metaheuristic algorithms with ANFIS to propose the best estimation model. However, many researchers only focused on the experiment without the demonstration mathematical or indicating which characteristic of optimization algorithm, during the run, affect and settable in coordination with ANFIS. The paper provides an adaption of metaheuristic algorithms with ANFIS which has been performed by considering accuracy parameters in layer 1 and layer 4 for the estimation problem. It is integrated six well-known metaheuristic algorithms and extracts their characteristic of them. In the experiment, the metaheuristic algorithms based on the evolutionary computation have been demonstrated more stable than swarm intelligence methods in tuning parameters of ANFIS.

Keyphrases: ANFIS, Crossover, Genetic Algorithm, Metaheuristics Algorithm, Mutation

@booklet{EasyChair:7594,
  author    = {Aref Yelghi and Shirmohammad Tavangari},
  title     = {Features of Metaheuristic Algorithm for Integration with ANFIS Model},
  howpublished = {EasyChair Preprint 7594},
  year      = {EasyChair, 2022}}