A novel meta-heuristic optimization algorithm: Thermal exchange optimization
By Ali Kaveh, Armin Dadras. Advances in Engineering Software, 2017
603 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/41495d55d3d6ff08616512bf3fedf5ed11feb1ff
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
- Tansel Dökeroğlu
- Laith Mohammad Abualigah
- Amir H. Gandomi
- Ahmet Cosar
- Tayfun Kucukyilmaz
- Ender Sevinç
- Seyedali Mirjalili
- Absalom El-Shamir Ezugwu
In Computational Theory and Mathematics
- Tansel Dökeroğlu
- Laith Mohammad Abualigah
- Amir H. Gandomi
- Ahmet Cosar
- Tayfun Kucukyilmaz
- Ender Sevinç
- Seyedali Mirjalili
- Essam H. Houssein
Citations in the corpus
- A survey on new generation metaheuristic algorithms (2019, background): “Oftadeh et al. (2010) propose Hunting Search (HuS) inspired by a set of hunter animals such as wolves, dolphins, and lions.”
- Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems (2020, background): “Essentially, the laws of physics have already produced significant outcomes when formulated into optimization techniques; examples are: thermal exchange optimization (TEO) [29] utilizes Newtonian’s laws of cooling, lightening search algorithm (LSA) [30] is inspired by the lightening phenomena in nat”