Data-driven artificial bee colony algorithm based on radial basis function neural network

Abstract

Search strategies play an essential role in artificial bee colony (ABC) algorithm. Different optimisation problems and search stages may need different search strategies. However, it is difficult to choose an appropriate search strategy. To tackle this issue, this paper proposes a data-driven ABC algorithm based on radial basis function neural network (called RBF-ABC). Firstly, a strategy pool with three distinct search strategies is built. The radial basis function (RBF) network is applied to evaluate offspring generated by the search strategies. The search strategy with the best evaluation value is used to guide the search. Dimension perturbation is employed to update multiple dimensions simultaneously, and it improves the convergence speed and the accuracy of the surrogate model. A set of 22 classical benchmark problems with 30 and 100 dimensions are utilised to verify the performance of RBF-ABC. Results show RBF-ABC can effectively save computational evaluations and outperform six other ABC algorithms.