Article
Version 1
Preserved in Portico This version is not peer-reviewed
Improving the Giant Armadillo Optimization Method
Version 1
: Received: 26 April 2024 / Approved: 26 April 2024 / Online: 26 April 2024 (14:09:40 CEST)
A peer-reviewed article of this Preprint also exists.
Kyrou, G.; Charilogis, V.; Tsoulos, I.G. Improving the Giant-Armadillo Optimization Method. Analytics 2024, 3, 225-240. Kyrou, G.; Charilogis, V.; Tsoulos, I.G. Improving the Giant-Armadillo Optimization Method. Analytics 2024, 3, 225-240.
Abstract
Global optimization is widely adopted nowadays in a variety of practical and scientific problems. In this context, a group of techniques that is widely used is that of evolutionary techniques. A relatively new evolutionary technique in this direction is that of Giant Armadillo Optimization, which is based on the hunting strategy of giant armadillos. In this paper, a number of modifications to this technique are proposed, such as the periodic application of a local minimization method as well as the use of modern termination techniques based on statistical observations. The proposed modifications have been tested on a wide - series test functions, available from the relevant literature and it was compared against other evolutionary methods.
Keywords
Global optimization; evolutionary methods; stochastic methods
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
