Bio-inspired computation: Where we stand and what's next

By Javier Del Ser, Eneko Osaba, Daniel Molina, Xin-She Yang, Sancho Salcedo-Sanz, David Camacho, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, Carlos Artemio Coello Coello, Francisco Herrera. Swarm and Evolutionary Computation, 2019

551 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/915b19303a590b5ff8c1772856535d447c3566b1

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Citations in the corpus

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “(2009) 244 Great Deluge Algorithm (GDA) Dueck (1993) 245 Greedy Politics Optimization (GPO) Melvix (2014) 246 Grenade Explosion Method (GEM) Ahrari and Atai (2010) 247 Grey Wolf Optimizer (GWO) Mirjalili et al.”
An improved grey wolf optimizer for solving engineering problems (2021, background): “1, in the literature (Del Ser et al., 2019; Mirjalili et al., 2016), these algorithms are classified into three categories: evolutionary, physics-based, and swarm intelligence algorithms.”
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, background): “…wide acknowledgment of the research community around these problems has ignited a shift of focus toward hybridizing deep learning models with elements from other areas of computational intelligence [154], with an emphasis lately placed on the use of evolutionary computation and swarm intelligence.”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, methodology): “In any case, and in order to avoid any ambiguity related to the terminology used [81, 26], the way in which individuals evolve along the execution should be detailed using a standard mathematical language [5].”