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

Cheng-Hung Chen, Yao-Cheng Tsai, and Rong-Zuo Jhang
Department of Electrical Engineering, National Formosa University, Taiwan
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

Cite: Cheng-Hung Chen, Yao-Cheng Tsai, and Rong-Zuo Jhang, "Approximation of the Piecewise Function Using Neural Fuzzy Networks with an Improved Artificial Bee Colony Algorithm," Jounal of Automation and Control Engineering, Vol. 4, No. 1, pp. 18-21, February, 2016. doi: 10.12720/joace.4.1.18-21
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