Bio-inspired computation: where we stand and what's next

Del Ser, Javier ORCID: https://orcid.org/0000-0002-1260-9775, Osaba, Eneko ORCID: https://orcid.org/0000-0001-7863-9910, Molina, Daniel ORCID: https://orcid.org/0000-0002-4175-2204, Yang, Xin-She ORCID: https://orcid.org/0000-0001-8231-5556, Salcedo-Sanz, Sancho, Camacho, David ORCID: https://orcid.org/0000-0002-5051-3475, Das, Swagatam, Suganthan, Ponnuthurai N., Coello Coello, Carlos A. and Herrera, Francisco ORCID: https://orcid.org/0000-0002-7283-312X (2019) Bio-inspired computation: where we stand and what's next. Swarm and Evolutionary Computation, 48 . pp. 220-250. ISSN 2210-6502

[img] PDF - Final accepted version (with author's formatting)
Restricted to Repository staff and depositor only until 29 April 2020.
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives.

Download (901kB) |

Abstract

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.

Item Type: Article
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 26767
Notes on copyright: © 2019. This accepted manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Useful Links:
Depositing User: Xin-She Yang
Date Deposited: 10 Jun 2019 11:59
Last Modified: 12 Jun 2019 13:04
URI: https://eprints.mdx.ac.uk/id/eprint/26767

Actions (login required)

Edit Item Edit Item

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year