دانلود رایگان مقاله الگوریتم تطبیقی و ترکیبی کلونی زنبور مصنوعی برای آموزش ANFIS

ABSTRACT

 In this study, we propose an Adaptive and Hybrid Artificial Bee Colony (aABC) algorithm to train ANFIS. Unlike the standard ABC algorithm, two new parameters are utilized in the solution search equation. These are arithmetic crossover rate and adaptivity coefficient. aABC algorithm gains the rapid convergence feature with the usage of arithmetic crossover and it is applied on two different problem groups and its performance is measured. Firstly, it is performed over 10 numerical ‘benchmark functions’. The results show that aABC algorithm is more efficient than standard ABC algorithm. Secondly, ANFIS is trained by using aABC algorithm to identify the nonlinear dynamic systems. Each application begins with the randomly selected initial population and then average RMSE is obtained. For four examples considered in ANFIS training, train error values are respectively computed as 0.0344, 0.0232, 0.0152 and 0.0205. Also, test error values for these examples are respectively found as 0.0255, 0.0202, 0.0146 and 0.0295. Although it varies according to the examples, performance increase between 4.51% and 33.33% occurs. Additionally, it is seen that aABC algorithm converges bettter than ABC algorithm in the all examples. The obtained results are compared with the neuro-fuzzy based approaches which are commonly used in the literature and it is seen that the proposed ABC variant can be efficiently used for ANFIS training.

5. Conclusion

In this study, the standard ABC algorithm is modified for training ANFIS and a variant of ABC named adaptive and hybrid ABC −aABC- is suggested. The adaptivity value which is developed basing on failure counter and the arithmetic crossover is added to the solution generating mechanism of aABC algorithm. In this manner, two new control parameters are adapted as crossover rate () and adaptivity coefficient (). The success of the suggested variant of ABC is tested on benchmark unconstrained numerical function and ANFIS training to identify nonlinear dynamical systems. The results are compared with other methods existing in the literature. In case that  and  parameters are settled properly on both application groups, it is seen that the convergence speed increases and better solutions are provided.