TY - JOUR
T1 - Bio-inspired computation
T2 - Where we stand and what's next
AU - Del Ser, Javier
AU - Osaba, Eneko
AU - Molina, Daniel
AU - Yang, Xin She
AU - Salcedo-Sanz, Sancho
AU - Camacho, David
AU - Das, Swagatam
AU - Suganthan, Ponnuthurai N.
AU - Coello Coello, Carlos A.
AU - Herrera, Francisco
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
KW - Benchmarks
KW - Bio-inspired computation
KW - Computationally expensive optimization
KW - Distributed evolutionary computation
KW - Dynamic optimization
KW - Ensembles
KW - Evolutionary computation
KW - Hyper-heuristics
KW - Large-scale global optimization
KW - Many-objective optimization
KW - Memetic algorithms
KW - Multi-modal optimization
KW - Multi-objective optimization
KW - Nature-inspired computation
KW - Parameter adaptation
KW - Parameter tuning
KW - Surrogate model assisted optimization
KW - Swarm intelligence
KW - Topologies
UR - https://www.scopus.com/pages/publications/85065055789
U2 - 10.1016/j.swevo.2019.04.008
DO - 10.1016/j.swevo.2019.04.008
M3 - Article
AN - SCOPUS:85065055789
SN - 2210-6502
VL - 48
SP - 220
EP - 250
JO - Swarm and Evolutionary Computation
JF - Swarm and Evolutionary Computation
ER -