Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/143184
Title: | Bio-inspired computation : where we stand and what’s next | Authors: | Del Ser, Javier Osaba, Eneko Molina, Daniel Yang, Xin-She Salcedo-Sanz, Sancho Camacho, David Das, Swagatam Suganthan, Ponnuthurai Nagaratnam Coello, Carlos A. Coello Herrera, Francisco |
Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2019 | Source: | Del Ser, J., Osaba, E., Molina, D., Yang, X.-S., Salcedo-Sanz, S., Camacho, D., . . . Herrera, F. (2019). Bio-inspired computation : where we stand and what’s next. Swarm and Evolutionary Computation, 48, 220-250. doi:10.1016/j.swevo.2019.04.008 | Journal: | Swarm and Evolutionary Computation | Abstract: | In recent years, the research community has witnessed an explosion of literature dealing with the mimicking 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. | URI: | https://hdl.handle.net/10356/143184 | ISSN: | 2210-6502 | DOI: | 10.1016/j.swevo.2019.04.008 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 2019 Elsevier B.V. All rights reserved. This paper was published in Swarm and Evolutionary Computation and is made available with permission of Elsevier B.V. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Journal Articles |
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