A Brief Review of Nature-Inspired Algorithms for Optimization

By Iztok Fister, Xin-She Yang, Janez Brest, Dusan Fister. arXiv.org, 2013

674 citations recorded by the source corpus.

Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very effi cient and thus have become popular tools for solving real-world problems. Some algorithms are insuffici ently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/9f2b23818faf29412faeecef21cfbaa0d28cd9e6

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Citations in the corpus

An improved grey wolf optimizer for solving engineering problems (2021, background): “Most of the real-world optimization problems have several difficulties, including but not limited to, high computational cost, non-linear constraints, non-convex search landscape, dynamic/noisy objective functions, and large solution space (Fister Jr et al., 2013).”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, methodology): “A detailed list and classification of metaheuristic algorithms are provided in [120].”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, methodology): “Fister Jr et al. (2013) have classified it into four categories as swarm intelligence (SI) based algorithms, bio-inspired (not SI) based algorithms, physicchemistry based algorithms, and the rest as another algorithm, whereas Siddique and Adeli (2015) have divided it into three subgroups as physics”
Monarch butterfly optimization (2015, background): “Swarm intelligence (SI) concerns the collective, emerging behavior of multiple, interacting agents who follow some simple rules [5].”
Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, background): “The majority of nature-inspired algorithms are meta-heuristics [126].”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, background): “The latter does not include other bio-inspired optimization algorithms [18], which do not have the swarm concept, e.”
Butterfly optimization algorithm: a novel approach for global optimization (2018, background): “With the time, these natural systems have become so robust and efﬁcient that they can solve most of the real-world problems (Fister et al. 2013).”