Journal article 365 views 64 downloads
Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources
,
Fabio Caraffini ,
Giovanni Iacca
International Journal of Intelligent Systems, Volume: 2023, Pages: 1 - 32
Swansea University Author:
Fabio Caraffini
-
PDF | Version of Record
Copyright © 2023 Souheila Khalfi et al. Distributed under the terms of a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Download (948.26KB)
DOI (Published version): 10.1155/2023/5708085
Abstract
In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of in...
| Published in: | International Journal of Intelligent Systems |
|---|---|
| ISSN: | 0884-8173 1098-111X |
| Published: |
Hindawi Limited
2023
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa64958 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have been thoroughly reviewed in several recent survey papers. However, the present survey addresses an important gap in the literature. Here, we reflect on a systematic categorisation of what we call “lightweight” metaheuristics, i.e., optimisation algorithms characterised by purposely limited memory and computational requirements. We focus mainly on two classes of lightweight algorithms: single-solution metaheuristics and “compact” optimisation algorithms. Our analysis is mostly focused on single-objective continuous optimisation. We provide an updated and unified view of the most important achievements in the field of lightweight metaheuristics, background concepts, and most important applications. We then discuss the implications of these algorithms and the main open questions and suggest future research directions. |
|---|---|
| College: |
Faculty of Science and Engineering |
| Start Page: |
1 |
| End Page: |
32 |