Del Ser, JavierOsaba, EnekoMolina, DanielYang, Xin SheSalcedo-Sanz, SanchoCamacho, DavidDas, SwagatamSuganthan, Ponnuthurai N.Coello Coello, Carlos A.Herrera, Francisco2019-08Del Ser , J , Osaba , E , Molina , D , Yang , X S , Salcedo-Sanz , S , Camacho , D , Das , S , Suganthan , P N , Coello Coello , C A & Herrera , F 2019 , ' Bio-inspired computation : Where we stand and what's next ' , Swarm and Evolutionary Computation , vol. 48 , pp. 220-250 . https://doi.org/10.1016/j.swevo.2019.04.0082210-6502Publisher Copyright: © 2019 Elsevier B.V.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.31enginfo:eu-repo/semantics/openAccessjournal article10.1016/j.swevo.2019.04.008BenchmarksBio-inspired computationComputationally expensive optimizationDistributed evolutionary computationDynamic optimizationEnsemblesEvolutionary computationHyper-heuristicsLarge-scale global optimizationMany-objective optimizationMemetic algorithmsMulti-modal optimizationMulti-objective optimizationNature-inspired computationParameter adaptationParameter tuningSurrogate model assisted optimizationSwarm intelligenceTopologiesGeneral Computer ScienceGeneral MathematicsSDG 9 - Industry, Innovation, and Infrastructurehttp://www.scopus.com/inward/record.url?scp=85065055789&partnerID=8YFLogxK