An Improved Gbest Guided Artificial Bee Colony Algorithm for Classification and Prediction Tasks

Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Researchers used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron (MLP). Normally Gbest Guided Artificial Bee Colony (GGABC) algorithm has strong exploration process for soliving mathmatical problems, however the poor exploration procedure create problems like slow convergence and trapping in local minima. For recovering the above gaps, the Improved Gbest Guided Artificial Bee Colony (IGGABC) algorithm is proposed for finding global optima. The proposed IGGABC algorithm has strong exploitation and exploration process. The experimental results show that IGGABC algorithm performs better than that standard GGABC, ABC and BP algorithm for Boolean data classification and time-series data prediction tasks.