Bio-inspired Computation: Where We Stand and What’s Next Del Ser, Javier Osaba, Eneko Molina Cabrera, Daniel Yang, Xin-She Salcedo-Sans, Sancho Camacho, David Swagatam, Das Ponnuthurai N., Suganthan Coello Coello, Carlos A. Herrera Triguero, Francisco 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 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. 2024-01-24T12:44:56Z 2024-01-24T12:44:56Z 2019-08 journal article J. Del Ser et al., «Bio-inspired computation: Where we stand and what’s next», Swarm and Evolutionary Computation, vol. 48, pp. 220-250, ago. 2019, doi: 10.1016/j.swevo.2019.04.008. https://hdl.handle.net/10481/87213 10.1016/j.swevo.2019.04.008 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ embargoed access Attribution-NonCommercial-NoDerivatives 4.0 Internacional