An Enhanced Grey Wolf Optimizer with Elite Inheritance and Balance Search Mechanisms
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909164922470400 |
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| author | Jiang, Jianhua Zhao, Ziying Li, Weihua Li, Keqin |
| author_facet | Jiang, Jianhua Zhao, Ziying Li, Weihua Li, Keqin |
| contents | The Grey Wolf Optimizer (GWO) is recognized as a novel meta-heuristic algorithm inspired by the social leadership hierarchy and hunting mechanism of grey wolves. It is well-known for its simple parameter setting, fast convergence speed, and strong optimization capability. In the original GWO, there are two significant design flaws in its fundamental optimization mechanisms. Problem (1): the algorithm fails to inherit from elite positions from the last iteration when generating the next positions of the wolf population, potentially leading to suboptimal solutions. Problem (2): the positions of the population are updated based on the central position of the three leading wolves (alpha, beta, delta), without a balanced mechanism between local and global search. To tackle these problems, an enhanced Grey Wolf Optimizer with Elite Inheritance Mechanism and Balance Search Mechanism, named as EBGWO, is proposed to improve the effectiveness of the position updating and the quality of the convergence solutions. The IEEE CEC 2014 benchmark functions suite and a series of simulation tests are employed to evaluate the performance of the proposed algorithm. The simulation tests involve a comparative study between EBGWO, three GWO variants, GWO and two well-known meta-heuristic algorithms. The experimental results demonstrate that the proposed EBGWO algorithm outperforms other meta-heuristic algorithms in both accuracy and convergence speed. Three engineering optimization problems are adopted to prove its capability in processing real-world problems. The results indicate that the proposed EBGWO outperforms several popular algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06524 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Enhanced Grey Wolf Optimizer with Elite Inheritance and Balance Search Mechanisms Jiang, Jianhua Zhao, Ziying Li, Weihua Li, Keqin Neural and Evolutionary Computing Artificial Intelligence The Grey Wolf Optimizer (GWO) is recognized as a novel meta-heuristic algorithm inspired by the social leadership hierarchy and hunting mechanism of grey wolves. It is well-known for its simple parameter setting, fast convergence speed, and strong optimization capability. In the original GWO, there are two significant design flaws in its fundamental optimization mechanisms. Problem (1): the algorithm fails to inherit from elite positions from the last iteration when generating the next positions of the wolf population, potentially leading to suboptimal solutions. Problem (2): the positions of the population are updated based on the central position of the three leading wolves (alpha, beta, delta), without a balanced mechanism between local and global search. To tackle these problems, an enhanced Grey Wolf Optimizer with Elite Inheritance Mechanism and Balance Search Mechanism, named as EBGWO, is proposed to improve the effectiveness of the position updating and the quality of the convergence solutions. The IEEE CEC 2014 benchmark functions suite and a series of simulation tests are employed to evaluate the performance of the proposed algorithm. The simulation tests involve a comparative study between EBGWO, three GWO variants, GWO and two well-known meta-heuristic algorithms. The experimental results demonstrate that the proposed EBGWO algorithm outperforms other meta-heuristic algorithms in both accuracy and convergence speed. Three engineering optimization problems are adopted to prove its capability in processing real-world problems. The results indicate that the proposed EBGWO outperforms several popular algorithms. |
| title | An Enhanced Grey Wolf Optimizer with Elite Inheritance and Balance Search Mechanisms |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2404.06524 |