Abstract
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.
Highlights
In the engineering profession, optimization methods and algorithms are becoming essential tools
At first glance, seemingly quite complex Krill Herd Algorithm (KHA) can be employed to obtain very quickly, satisfactory results for artificial neural network (ANN) training
We believe that the proposed method represents a promising tool for neural classification
Summary
In the engineering profession, optimization methods and algorithms are becoming essential tools. This is due to the need for extensive computational power when deriving solutions through their enlistment, and rests as well in the nature of the properties of the employed methods and algorithms, themselves. The methods that are currently used (to very good effect) in deriving solutions. Łukasik to problems of optimization, are the gradient methods and the heuristic algorithms. Both procedures, in addition to their advantages, have some drawbacks either
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