An improved grey wolf optimizer for solving engineering problems

By Mohammad-Hossein Nadimi-Shahraki, Shokooh Taghian, S. Mirjalili. Expert systems with applications, 2021

1,021 citations recorded by the source corpus.

Abstract In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The I-GWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-of-the-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/690ddcd6e34af560da7cbb06674341a96ad2283b

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. Jeffrey O. Agushaka
3. Laith Mohammad Abualigah
4. Ali Diabat

In Computational Theory and Mathematics

1. Jeffrey O. Agushaka
2. Laith Mohammad Abualigah
3. Absalom El-Shamir Ezugwu

Citations in the corpus

• Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer (2022, background): “Many researchers have proposed novel or hybrid methods to find optimal solutions to these problems [15, 27, 29, 46, 51, 56, 58].”