Kyrou, G.; Charilogis, V.; G.Tsoulos, I. Improving the Giant Armadillo Optimization Method. Preprints2024, 2024041784. https://doi.org/10.20944/preprints202404.1784.v1
APA Style
Kyrou, G., Charilogis, V., & G.Tsoulos, I. (2024). Improving the Giant Armadillo Optimization Method. Preprints. https://doi.org/10.20944/preprints202404.1784.v1
Chicago/Turabian Style
Kyrou, G., Vasileios Charilogis and Ioannis G.Tsoulos. 2024 "Improving the Giant Armadillo Optimization Method" Preprints. https://doi.org/10.20944/preprints202404.1784.v1
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.
