Training Multi-Layer Perceptron using Opposition based Learning Spiral Optimization Algorithm
AbstractIn this study, the Opposition based learning Spiral Optimization Algorithm (OBLSOA) is presented for training Multi-Layer Perceptron (MLP). The main idea of Spiral Optimization Algorithm (SOA) is based on the dynamic step dimension in its spiral path trajectory. The primary opposition based learning (OBL) concept first was come from the Yin-Yang symbol in the ancient Chinese philosophy. According to OBL concept, if a candidate point is far from the solution, the opposite point of this candidate can be closer to the solution than that point. We applied OBL concept to spiral optimization algorithm for training MLP. OBLSOA comprises two main stages: the first is the opposition-based learning population initialization and the other is opposition-based learning generation jumping. To evaluate the performance of the proposed OBLSOA, we used eight standard datasets including four classification datasets (XOR, balloon, Iris, breast cancer) and three function-approximation datasets (sigmoid, cosine, and sine). The performance proposed OBLSOA was compared with the original SOA for all datasets in terms of the Mean Square Error (MSE) metric. The training and test results show that the proposed OBLSOA is able to be provide very competitive and effective in training MLPs.