An improved grey wolf optimizer for solving engineering problems

• Published in: Expert systems with applications Vol. 166; p. 113917
• Main Authors: Nadimi-Shahraki, Mohammad H., Taghian, Shokooh, Mirjalili, Seyedali
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.03.2021; Elsevier BV
• Subjects: Algorithm; Algorithms; Artificial intelligence; Design engineering; Design optimization; Engineering optimization problems; Global optimization; Grey wolf optimizer; Hunting; Improved grey wolf optimizer; Machine learning; Metaheuristic; Optimization; Search methods; Statistical tests; Strategy; Swarm intelligence algorithm; Engineering optimization problems; Swarm intelligence algorithm; Metaheuristic; Improved grey wolf optimizer; Grey wolf optimizer; Algorithm; Artificial intelligence; Optimization
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113917

[Display omitted] •Proposing an improved Grey Wolf Optimizer (I-GWO) for solving engineering problems.•Introducing a new search strategy named dimension learning-based hunting (DLH).•DLH is to enhance balance between local and global search and maintain diversity.•Performance of I-GWO is evaluated on the CEC2018 and three engineering problems.•I-GWO algorithm is very competitive and superior to the compared algorithms. 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.