An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems
Abstract As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the optimization problems in various engineering fields. In order to further improve the convergence spee...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-64526-2 |
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| author | Yihui Qiu Xiaoxiao Yang Shuixuan Chen |
| author_facet | Yihui Qiu Xiaoxiao Yang Shuixuan Chen |
| author_sort | Yihui Qiu |
| collection | DOAJ |
| description | Abstract As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the optimization problems in various engineering fields. In order to further improve the convergence speed, solution accuracy, and local minima escaping ability of the traditional GWO algorithm, this work proposes a multi-strategy fusion improved gray wolf optimization (IGWO) algorithm. First, the initial population is optimized using the lens imaging reverse learning algorithm for laying the foundation for global search. Second, a nonlinear control parameter convergence strategy based on cosine variation is proposed to coordinate the global exploration and local exploitation ability of the algorithm. Finally, inspired by the tunicate swarm algorithm (TSA) and the particle swarm algorithm (PSO), a nonlinear tuning strategy for the parameters, and a correction based on the individual historical optimal positions and the global optimal positions are added in the position update equations to speed up the convergence of the algorithm. The proposed algorithm is assessed using 23 benchmark test problems, 15 CEC2014 test problems, and 2 well-known constraint engineering problems. The results show that the proposed IGWO has a balanced E&P capability in coping with global optimization as analyzed by the Wilcoxon rank sum and Friedman tests, and has a clear advantage over other state-of-the-art algorithms. |
| format | Article |
| id | doaj-art-03da5b1e791c428fb89f4801056656a6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-03da5b1e791c428fb89f4801056656a62025-01-26T12:34:52ZengNature PortfolioScientific Reports2045-23222024-06-0114112410.1038/s41598-024-64526-2An improved gray wolf optimization algorithm solving to functional optimization and engineering design problemsYihui Qiu0Xiaoxiao Yang1Shuixuan Chen2School of Economics and Management, Xiamen University of TechnologySchool of Economics and Management, Xiamen University of TechnologySchool of Mechanical and Automotive Engineering, Xiamen University of TechnologyAbstract As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the optimization problems in various engineering fields. In order to further improve the convergence speed, solution accuracy, and local minima escaping ability of the traditional GWO algorithm, this work proposes a multi-strategy fusion improved gray wolf optimization (IGWO) algorithm. First, the initial population is optimized using the lens imaging reverse learning algorithm for laying the foundation for global search. Second, a nonlinear control parameter convergence strategy based on cosine variation is proposed to coordinate the global exploration and local exploitation ability of the algorithm. Finally, inspired by the tunicate swarm algorithm (TSA) and the particle swarm algorithm (PSO), a nonlinear tuning strategy for the parameters, and a correction based on the individual historical optimal positions and the global optimal positions are added in the position update equations to speed up the convergence of the algorithm. The proposed algorithm is assessed using 23 benchmark test problems, 15 CEC2014 test problems, and 2 well-known constraint engineering problems. The results show that the proposed IGWO has a balanced E&P capability in coping with global optimization as analyzed by the Wilcoxon rank sum and Friedman tests, and has a clear advantage over other state-of-the-art algorithms.https://doi.org/10.1038/s41598-024-64526-2Grey wolf algorithmFunction optimizationEngineering design optimizationExploration and exploitation |
| spellingShingle | Yihui Qiu Xiaoxiao Yang Shuixuan Chen An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems Scientific Reports Grey wolf algorithm Function optimization Engineering design optimization Exploration and exploitation |
| title | An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems |
| title_full | An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems |
| title_fullStr | An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems |
| title_full_unstemmed | An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems |
| title_short | An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems |
| title_sort | improved gray wolf optimization algorithm solving to functional optimization and engineering design problems |
| topic | Grey wolf algorithm Function optimization Engineering design optimization Exploration and exploitation |
| url | https://doi.org/10.1038/s41598-024-64526-2 |
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