Approximation of the Piecewise Function Using Neural Fuzzy Networks with an Improved Artificial Bee Colony Algorithm

Abstract

The artificial bee colony (ABC) algorithm is inspired by the behavior of honey bees. It is a relatively new optimization algorithm that has been proved competitive with conventional biology-inspired algorithms. The IABC algorithm is used, with the differential evolution (DE) algorithm added to the new solution search equation of ABC, to improve convergence speed. The IABC adopts the reward-based roulette wheel selection mechanism initially to divide all solutions suitably into feasible and infeasible solutions; thereafter, it divides them based on feasible and infeasible solutions for the implementation of incentives and punishments. Finally, the proposed method is applied to nonlinear system control problems. The experimental results of this study demonstrate the performance of IABC against that of other algorithms in nonlinear problems.  Index Terms-artificial bee colony algorithm, differential evolution, neural fuzzy networks, nonlinear system problems, reward-based roulette wheel selection

Archives of Mining Sciences

“…Bahram and Nader [23] combined ABC with radial basis function (rBf) and backpropagation (BP) neural network to predict phosphate ore grade. Chen et al [24] introduced the differential evolution (dE) algorithm and ABC algorithm into the new ABC search equation in order to improve the convergence speed of the algorithm. Anuar [25] combines the artificial neural network with ABC to apply the proposed algorithm to the classification of criminal data while avoiding the problems that neural networks can easily fall into the local optima.…”