An improved grey wolf optimizer for solving engineering problems #22
Description
📋 논문의 정보를 알려주세요.
- An improved grey wolf optimizer for solving engineering problems
- Mohammad H. Nadimi-Shahraki et al
- Expert Systems With Applications
- 2020-10-16
📃 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.
🔎 어떤 논문인지 소개해주세요.
- GWO의 창시자 Seyedali Mirjalili가 참여한 GWO의 최신 개선 버젼입니다.
- 이 개선은 다양성 부족, Exploitation과 Exploration의 불균형, 조기수렴을 완화합니다.
🔑 핵심 키워드를 적어주세요.
- Improved grey wolf optimizer, Engineering optimization problems