@article{DELSER2019220,
title = "Bio-inspired computation: Where we stand and what's next",
journal = "Swarm and Evolutionary Computation",
volume = "48",
pages = "220 - 250",
year = "2019",
issn = "2210-6502",
doi = "https://doi.org/10.1016/j.swevo.2019.04.008",
url = "http://www.sciencedirect.com/science/article/pii/S2210650218310277",
author = "Javier Del Ser and Eneko Osaba and Daniel Molina and Xin-She Yang and Sancho Salcedo-Sanz and David Camacho and Swagatam Das and Ponnuthurai N. Suganthan and Carlos A. Coello Coello and Francisco Herrera",
keywords = "Bio-inspired computation, Evolutionary computation, Swarm intelligence, Nature-inspired computation, Dynamic optimization, Multi-objective optimization, Many-objective optimization, Multi-modal optimization, Large-scale global optimization, Topologies, Ensembles, Hyper-heuristics, Surrogate model assisted optimization, Computationally expensive optimization, Distributed evolutionary computation, Memetic algorithms, Parameter tuning, Parameter adaptation, Benchmarks",
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."
}