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
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: embargo_20211231
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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