Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems

By Gaige Wang, Suash Deb, Leandro dos S. Coelho. International Journal of Bio-Inspired Computation (IJBIC), 2018

385 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Information Systems

View Paper: https://www.semanticscholar.org/paper/72ba28d32afbe0337cabe8e5ed289e7037ec00ae

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Absalom El-Shamir Ezugwu
2. Tong Han
3. Olaide Nathaniel Oyelade
4. Zhuoran Zhang
5. Andi Tang
6. Bo Han
7. Lei Xie
8. Huan Zhou

In Genetics

1. Tong Han
2. Zhuoran Zhang
3. Andi Tang
4. Bo Han
5. Lei Xie
6. Huan Zhou
7. Laith Mohammad Abualigah
8. Absalom El-Shamir Ezugwu

In Information Systems

1. Laith Mohammad Abualigah
2. Absalom El-Shamir Ezugwu

Citations in the corpus

• Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm (2022, background): “Earthworm Optimisation Algorithm (EOA), also referred to as EWA, is a metaheuristic algorithm whose inspiration was drawn from the reproductive nature of the earthworm [61].”
• Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization (2021, methodology): “Some other algorithms of this category include: grey wolf optimizer (GWO) [30], monarch butterfly optimization (MBO) [31], elephant herding optimization (EHO) [32], moth search algorithm (MSA) [33], manta ray foraging optimization (MRFO) [34],earthworm optimization algorithm (EOA) [35], etc.”