Training Multi-Layer Perceptron Using Harris Hawks Optimization

Abstract

In this paper, Harris hawks optimization (HHO) algorithm has been proposed as an up-to-date meta-heuristic algorithm for training multi-layer perceptron (MLP). The performance of the HHO-based MLP trainer was tested by employing five standard data sets (XOR, Balloon, Iris, Breast Cancer and Heart). The results were compared with those obtained with the sine cosine algorithm (SCA). Comparative statistical results showed that using HHO algorithm as a trainer is more effective and has a higher rate of classification ability.