Abstract:

The artificial bee colony (ABC) algorithm attracts wide attention because of its simple process, a small number of control parameters, strong global convergence ability and easy implementation. However, it has several disadvantages, such as low convergence precision and slow convergence speed. Inspired by the rules of the optimal biological individual in a neighborhood, we propose a neighborhood search artificial colony (NABC) algorithm to address these issues. It improves the searching speed of the population by searching the food source around the best food source in the neighborhood. Moreover, to dynamically adjust the search process of the algorithm, we also propose a dynamical neighborhood search ABC (DNABC) algorithm based on trigonometric function regulatory factors. It can make the algorithm focus on a global search in the early stage and on depth search in the late stage of exploration. Results from the experiments on 12 benchmark functions show that the NABC algorithm has high convergence precision and fast convergence speed during function optimization. And the NABC algorithm can be improved by the adjustment factors of the trigonometric function.

Key words: artificial bee colony algorithm, dynamic adjustment factor, neighborhood optimal individual, convergence accuracy, convergence speed, function optimization