Use of PSO and Other Swarm Intelligence techniques to improve computational speed Research Papers (original) (raw)

In recent decades, artificial neural networks (ANNs) have been extensively applied in different areas such as engineering, medicine, business, education, manufacturing
and so on. Nowadays,ANNsare as a hot research in medicine
especially in the fields of medical disease diagnosis. To
have a high efficiency in ANN, selection of an appropriate
architecture and learning algorithm is very important. ANN
learning is a complex task and an efficient learning algorithm
has a significant role to enhance ANN performance. In
this paper, a new meta-heuristic algorithm, centripetal accelerated particle swarm optimization (CAPSO), is applied to evolve the ANN learning and accuracy. The algorithm is
based on an improved scheme of particle swarm algorithm
and Newton’s laws ofmotion. The hybrid learning ofCAPSO
and multi-layer perceptron (MLP) network, CAPSO-MLP, is
used to classify the data of nine standard medical datasets of Hepatitis, Heart Disease, Pima Indian Diabetes, Wisconsin
Prognostic Breast Cancer, Parkinson’s disease, Echocardiogram, Liver Disorders, Laryngeal 1 and Acute Inflammations. The performance of CAPSO-MLP is compared with those of PSO, gravitational search algorithm and imperialist competitive algorithm on MLP. The efficiency ofmethods are evaluated based on mean square error, accuracy, sensitivity, specificity, area under the receiver operating characteristics curve and statistical tests of t-test and Wilcoxon’s signed ranks test. The results indicate that CAPSO-MLP provides more effective performance than the others for medical disease diagnosis especially in term of unseen data (testing data) and datasets with high missing data values.