Multi-population cooperative bat algorithm-based optimization of artificial neural network model

The performance of an artificial neural network (ANN) depends on the connection weights and network structure. Many optimization algorithms have been applied for ANN model selection. This paper presents an optimization algorithm based on the cooperative bat-inspired algorithm. The advantage of the bat algorithm lies in the combination of a population-based algorithm and local search; however, it is more powerful in local search. Therefore to better balance exploration and exploitation in the population some modifications to the velocity equation of the standard bat algorithm are applied. In addition, we propose two new topologies for cooperation between subpopulations to further improve the algorithm’s capability to maintain the diversity of bats in the population. The first is a combination of two known mechanisms (Ring and Master–Slave), and the second inserts a Coevolving strategy of slave subpopulations in the Master–Slave strategy. The proposed methods are applied for the selection of an ANN model, where both the structure of the ANN and its weights are optimized by the method. Six classification and two time series prediction benchmark datasets are tested and the performance of the proposed algorithms is evaluated and compared with other methods in the literature. Statistical analysis shows that for the classification problem there is a significant improvement in the bat algorithm with Ring and Master–Slave strategies cooperation compared to the other methods in the literature in terms of classification error for three cases out of five and a significant enhancement in the number of connection weights in the network. The analysis also shows that for time series prediction there is a significant improvement in the prediction error for all the cases.