Training Neural Networks with Krill Herd Algorithm

• Published in: Neural processing letters Vol. 44; no. 1; pp. 5 - 17
• Main Authors: Kowalski, Piotr A., Łukasik, Szymon
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2016; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Back propagation; Classification; Complex Systems; Computational Intelligence; Computer Science; Food; Foraging behavior; Genetic algorithms; Heuristic; Heuristic methods; Krill; Machine learning; Mutation; Neural networks; Optimization; Propagation; Biologically Inspired Algorithm; Krill Herd Algorithm; Neural Networks; Metaheuristic; Optimization
• ISSN: 1370-4621; 1573-773X; 1573-773X
• DOI: 10.1007/s11063-015-9463-0

In recent times, several new metaheuristic algorithms based on natural phenomena have been made available to researchers. One of these is that of the Krill Herd Algorithm (KHA) procedure. It contains many interesting mechanisms. The purpose of this article is to compare the KHA optimization algorithm used for learning an artificial neural network (ANN), with other heuristic methods and with more conventional procedures. The proposed ANN training method has been verified for the classification task. For that purpose benchmark examples drawn from the UCI Machine Learning Repository were employed with Classification Error and Sum of Square Errors being used as evaluation criteria. It has been concluded that the application of KHA offers promising performance—both in terms of aforementioned metrics, as well as time needed for ANN training.